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Implementation of an experimental facility and modeling studies for time varying images. Jensen, Olav Velling 1973

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IMPLEMENTATION OF AN EXPERIMENTAL FACILITY AND MODELING STUDIES FOR TIME VARYING IMAGES by Olav Veiling Jensen B.Sc.(E.E.)» University of Alberta, 1971 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in the Department of E l e c t r i c a l Engineering We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA September 1973 In p r e s e n t i n g t h i s t h e s i s i n p a r t i a l f u l f i l m e n t o f the requirements f o r an advanced degree a t the U n i v e r s i t y o f B r i t i s h Columbia, I agree t h a t the L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r r e f e r e n c e and study. I f u r t h e r agree t h a t p e r m i s s i o n f o r e x t e n s i v e c o p y i n g o f t h i s t h e s i s f o r s c h o l a r l y purposes may be g r a n t e d by the Head o f my Department or by h i s r e p r e s e n t a t i v e s . I t i s understood t h a t c o p y i n g or p u b l i c a t i o n of t h i s t h e s i s f o r f i n a n c i a l g a i n s h a l l not be a llowed without my w r i t t e n p e r m i s s i o n . Department The U n i v e r s i t y o f B r i t i s h Columbia Vancouver 8, Canada Date Sepir 2 6 7 1973 Abstract A wealth of experiments have been performed studying image en-coding techniques as applied to non-time varying or single-frame images. However, to date l i t t l e work has been .done to apply these techniques to time varying images, with most of such works emphasizing various ad hoc redundancy reduction techniques. In this work, a computer based experimental system i s implem-ented which makes more methodological studies of time varying images pos-sible. Particular attention i s devoted to obtaining very accurate inter-frame registration and uniform quantization of the images. Using this system, a selection of 35 mm movie film images are digitized and stored on computer magnetic tape in a format compatible with many other comput-ing installations, providing a standard data base for future experiments. An ..often ..used .model . for ...describing , picture data i s the station-ary Gauss-Markov model. In this work, the appropriateness of this model for describing time varying images i s studied by comparing the autocorre-lation functions as described by the model and as obtained by computation from the picture data. These results indicate that the autocorrelation function is best described by a function which i s separable i n the time dimension and nonseparable in the spacial dimensions. A number of DPCM communication systems are then studied as a vehicle for evaluating the effect of using the Gauss-Markov model. These results indicate that, for the sample images studied here, the estimated performance using the Gauss-Markov model i s good when the model i s a good f i t to the f i r s t data point of the computed autocorrelation function. i i TABLE OF CONTENTS Page I. INTRODUCTION 1.1 Motivation of Study 1 1.2 Review of Previous Work 2 1.3 Scope of Thesis 4 I I . THE IMAGE DIGITIZER 2.1 Introduction 5 2.2 Film Transport and Hardware 6 2.3 Interframe R e g i s t r a t i o n Technique 13 2.4 Automatic Intensity Control f o r the F l y i n g Spot Scanner 18 2.5 The D i g i t i z e d Images 20 III. MODELS FOR IMAGES 3.1 Introduction 24 3.2 Stationary Models for Monochromatic Time Varying Images -. .. .. ... . . . . . • . . . . .24 3.3 Closeness of F i t C r i t e r i o n 27 3.4 The Source Images • 27 3.4.1 Subject One 33 3.4.2 Subject Two • 35 3.5 Measured Model Accuracy 35 IV. THE DPCM COMMUNICATION SYSTEM 4.1 Introduction • 53 4.2 System Model 53 4.3 Optimum Linear One-Dimensional P r e d i c t i o n 56 4.4 The Optimum Linear Two-Dimensional P r e d i c t o r 61 4.5 Signal-to-Noise Ratio Measurement . 63 V. CONCLUSION • • 70 APPENDIX 7 1 REFERENCES . • • 1 0 8 i i i LIST OF ILLUSTRATIONS Figure Page 2.1A Photograph, of 35 mm film transport - front view . . . . 8 2.IB Photograph, of 35 mm film transport - top view 9 2.1C Photograph of image digiti z i n g system - layout . . . . 10 2.ID Photograph of image digitizing system - close-up . . . 11 2.2 Flow diagram for the film advance algorithm 14 2.3 Standard dimensions for 35 mm motion picture film . . . 16 2.4 Sketch, of video waveform from single scan along perforations of 35 mm film and the associated decision waveform generated 17 2.5 Flow diagram for a general film processing algorithm . 19 2.6 Flow diagram for beam intensity control algorithm . . . 21 2.7 Flow diagram for the image digitizing algorithm . . . . 22 .3...1A .Photographs -of .selected .frames -f-rom-experimental image film strips 28 3. IB Photographs; of selected frames from experimental image film strips 29 3.1C Photographs of selected frames from experimental image film strips 30 3.ID Photographs of selected frames from experimental image film strips 31 3.IE Photographs fo selected frames from experimental image film strips 32 3.2 Average temporal correlation at a time lag of one frame versus the frame number of the f i r s t frame for Subject One 34 3.3 Average temporal correlation at a time lag of one frame versus the frame number of the f i r s t frame for Subject Two 36 3.4A Correlation in x-direction for (a) subject one (b) subject two 40 3.4B Correlation in y-direction for (a) subject one (b) subject two 41 i v P a 8 e 3.4C Correlation in time direction for (a) subject one (b) subject two 42 3.4D Correlation in :x-y-directi-on for (a) subject one (b) subject two 43 3.4E Correlation in x-t direction for (a) subject one (b) subject two 44 3.5A Predicted and computed diagonal autocorrelation functions for (a) subject one (b) subject two . . . 47 3.5B Predicted and computed diagonal autocorrelation functions for (a) subject one (b) subject two . . . 48 3.6A Predicted and computed diagonal autocorrelation functions for (a) subject one (b) subject two . . 51 3.6B Predicted and computed diagonal autocorrelation functions for (a) subject one (b) subject two . . 52 4.1 A DPCM communication system 54 2 2 4.2 oe /ax versus prediction coefficient for one-dimen-.sional -linear, predictor : 57 4.3 Signal-to-noise Ratio (SNR) versus prediction coefficient for one-dimensional linear predictor . . . 59 4.4 SNR degradation from optimum versus prediction coef-f i c i e n t deviation from optimum for one-dimensional predictor 60 4.5 Nearest previous samples for x. used for prediction . . 64 v LIST OF TABLES Table Page I Exponential constants for l e a s t square f i t t e d curves and the r e s u l t i n g e r r o r measure 45 II Model accuracy (D=-10 log-^Q ( M S E ) ) i n p r e d i c t i n g d i a -gonal autocorrelation based on (a) exponentially f i t t e d curves (b) computed data points 49 I I I Signal-to-noise r a t i o s predicted from model and measured f o r PDCM communication systems operating on subject one and two 66 IV Summary of exponential c o e f f i c i e n t s and measured c o r r e l a t i o n s used i n t h i s study 69 v i ACKNOWLEDGEMENT I would l i k e to express my gratitude f o r the encouragement and assistance I received from my research supervisor, Dr. G.B. Anderson I would also l i k e to thank Messrs, M. Koombes and J . Stuber whose tech-n i c a l assistance made th i s work p o s s i b l e , Mr. H.H. Black for the image reproductions used i n th i s t h e s i s , and Miss N. Duggan f o r typing the the s i s . I also acknowledge the f i n a n c i a l assistance received from the National Research Council of Canada through Grant NRC A-7994.-v i i . I. INTRODUCTION .1.1 Motivation of Study I t i s a w e l l known fact that p i c t u r e images contain- a large amount of redundancy and therefore any channel used to transmit these images must n e c e s s a r i l y operate w e l l below the maximum capacity f o r that channel. Therefore some form of source encoding should be p o s s i b l e which would reduce the bandwidth requirement f o r transmitting a source and thereby make more e f f i c i e n t use of the communication channel. This concept has motivated a wealth of experiemnts studying p o s s i b l e source encoding techniques f o r non-time varying or s i n g l e frame images. The major reason f o r the lack of experimentation with time varying images have been; 1) the added complexity of analysis when a t h i r d dimension i s added to the s i t u a t i o n , 2) the great d i f f i c u l t y i n obtaining good - q u a l i t y experimental source images, and 3) the already high cost, i n terms of both time and expense, of s i n g l e frame processing i s m u l t i -p l i e d when a la r g e r number of frames are used. With the development of more s o p h i s t i c a t e d technology; most notably f a s t e r , more r e l i a b l e , and l e s s expensive computing f a c i l i t i e s ; i t has recently become more p r a c t i c a l to consider extending known image processing techniques to trea t time varying images. However, to date very l i t t l e q uantitative work has been carried- out i n th i s area. Most work that has been reported has emphasized, subjective comparison of r e s u l t s obtained from using various ad hoc redundency reduction techniques. For these reasons, the motivation for the work i n t h i s thesis has been to develop an experimental computer based system capable of handling motion pictures and provide an appropriate data base f or studying time varying images, as w e l l as begin a more methodological study of 1 2 time varying image coding techniques. 1.2 Review of Related Work When any p h y s i c a l phenomona i s to be studied, i t i s u s u a l l y convenient to use a model, i n v o l v i n g only a few e s s e n t i a l parameters, to describe that phenomona. When a model which adequately describes the phenomona has been chosen, analysis of that phenomona becomes more t r a c -t i b l e . When the p h y s i c a l phenomona i s to be modeled as a random process, the mathematical description of that process w i l l be a s t a t i s t i c a l one. Kretmer [1] was one of the f i r s t to measure some of the s t a t -i s t i c s of a video s i g n a l produced by a t e l e v i s i o n camera. These s t a t -i s t i c s included the f i r s t order p r o b a b i l i t y d i s t r i b u t i o n of the ampli-tude of the video s i g n a l , the f i r s t order p r o b a b i l i t y d i s t r i b u t i o n of the e r r o r amplitude r e s u l t i n g from using a l i n e a r p r e d i c t i o n scheme, and the autocorrelation function of a t y p i c a l s i g n a l . More s i g n i f i c a n t l y , Kretzmer was one of the f i r s t to measure the a u t o c o r r e l a t i o n between two adjacent frames f o r a time varying image. Schreiber [2] has measured the second and t h i r d order probab-i l i t y d i s t r i b u t i o n s of the video s i g n a l amplitude as w e l l as some mea-sures of the second and t h i r d order entropies of t y p i c a l p i c t u r e images. Limb [3] has measured some f i r s t to fourth order entropies and concluded that most redundancy i s associated with the second order p r o b a b i l i t y density function f o r low d e t a i l images while higher order s t a t i s t i c s are responsible for the redundancies i n medium and high d e t a i l images. The time dimension,'was studied more s e r i o u s l y by Wallace [4] and by Seyler [ 5 , 6 ] who measured the f i r s t order p r o b a b i l i t y d i s t r i b u t i o n s f o r t e l e v i s i o n frame di f f e r e n c e s . 3 Measurement of some of the s t a t i s t i c s of video images made i t p ossible to develop a n a l y t i c a l l y t r a c t i b l e models which were consid-ered to be reasonable descriptions of these images. Franks [7] proposed a model f o r random pi c t u r e images and derived expressions based on t h i s model for the second order s t a t i s t i c s . One of the expressions was the power s p e c t r a l density which was found to be expressible as a product of three components each separately a r i s i n g from the inf l u e n c e of point to p o i n t , l i n e to l i n e , and frame to frame c o r r e l a t i o n . This model was the s t a t i o n a r y , wide sense Markov model which i s commonly used today. For his model, Franks assumed s e p a r a b i l i t y of the e f f e c t s of the dimensions of the sequence. This n e c e s s a r i l y produced a separable . au t o c o r r e l a t i o n function. Kretzmer's work [1], however, suggested, at l e a s t i n the s p a c i a l dimensions, that a nonseparable form might be more -app.rop-ri.afee,. This,.q.ues tion . w i l l be-discussed f u r t h e r -.in t h i s .thesis.. As has been previously mentioned, a wealth of l i t e r a t u r e e x i s t s on redundancy removal techniques f o r s t i l l or s i n g l e frame images. Of these studies, the more recent ones have been most notably concerned with l i n e a r transformations and block quantization [8] - [11], and d i f f e r e n t i a l pulse-code modulation (DPCM) encoders [12] - [15]. In the area of redundancy removal techniques applied to time varying images, much l e s s work has been done [16] - [19]. These works can be considered to be mainly ad hoc experiments since most researchers have applied purely subjective c r i t e r i o n f o r s e l e c t i n g t h e i r encoding methods and also for evaluating the r e s u l t s . Mounts [17], however, has applied a form of DPCM encoding whereby he used the sample i n the immed-i a t e l y previous frame as the predicted value and then encoded the d i f -ference s i g n a l . In t h i s thesis we determine the e f f e c t of coding 4 dimensionality and data model on DPCM coding efficiency for experimental data. 1.3 Scope of Thesis The purpose of this study i s to obtain a high quality data set which can be used for subsequent studies in image coding techniques and other communication system studies, to measure the autocorrelation func-tion of selected time varying monochromatic images and to examine the ab i l i t y of certain h i s t o r i c a l models to describe the autocorrelation function, and, f i n a l l y , to measure the signal to noise ratio (SNR) of DPCM systems when different types of prediction schemes are used. In chapter II the digitization system used to obtain a high quality data set i s discussed. The system design requirements are pre-sented, the evolution of the system into i t s f i n a l form is discussed, and the format of the digitized images as stored on computer magnetic tape i s described. In Chapter III a number of commonly used models for time varying images are discussed. The autocorrelation functions for two such.images are measured and compared to the model descriptions in order to evaluate the models. In Chapter IV several DPCM communication systems are examined i n order to evaluate the effect of using models i n determining theoretical system performances as compared to the measured performance with real data and to evaluate the effect of increased dimensionality on the system performance. 5 II. THE IMAGE DIGITIZER 2.1 Introduction In experimental studies i t i s necessary to have both an experi-mental system and also the material-or data on which the experiments w i l l be performed. If the picture material or data can be procured from an external source, then we need be concerned only with procuring or building an experimental system. If, as i n the case of this study, the test data i s not available in a form which can be used directly, then the preparation or interfacing of the picture material can become a significant part of the project. The main purpose of this chapter w i l l be to discuss the experimental picture processing system developed to digitize and study time dependent monochromatic images. .A (primary objective in designing, a high .quality digitizing system i s to achieve uniform sampling. For example, i t would be impossible to differentiate between motion caused by translation of an object i n an image and motion between frames caused by translation of the sampling points during digitization of the time varying image data. Similarly, errors would occur from non-uniform sampling i n the spacial dimensions. Maintaining the uniformity of sampling in the spacial dimen-sions had already been provided for prior to the beginning of this pro-ject i n that a digitizing system for single frame images had already been b u i l t . This system consisted of a precision flying spot scanner operated under the control of a Supernova minicomputer. The minicomputer controls the position of the spot on the screen of the display and i t s duration. This light spot i s then, focused on an image transparency and the resulting modulated light signal collected in a photomultiplier tube. The video signal produced by the photomultiplier i s integrated for the duration of the spot and this level i s then digitized and trans-mitted to the minicomputer for storage on magnetic tape. The problem then remaining to obtain digitized time varying images was a matter of applying this single frame processing to a sequence of frames i n such a way as to obtain precise interframe registration. At f i r s t i t was f e l t that the most simple solution might be to apply software registration*techniques. However this would require benchmarks to be added to each frame so that translational and rotational errors could be detected. Then individual frames could be corrected to some reference orientation provided the errors were small and the size of the images digitized were larger than the desired image size. It would «• then.:also vbe .^required .*to-.sample. ..the...image <at a rate sufficiently .high to make interpolation errors negligible when the reference sample points f e l l between the points actually sampled. This, of course, would s t i l l not solve the redisplay problem. This feature would be necessary i n future works involving subjective evaluation of results produced from applying image processing techniques. Redisplaying of the images would also require good quality interframe registration. In this case, the film would have to be held i n a very accurate position while the film was being exposed. Realizing this need, i t was proposed that i t would be possible to use only one device for both scanning and redisplay purposes. Then software registration could be dispensed with entirely. 2.2 The Film Transport and Hardware Photographs of the mechanical film transport designed to 7 provide high precision interframe registration and of the digitizing system are shown in figure 2.1. Lateral registration i s performed by two sets of two ro l l i n g bearings located at either end of the image plane. The two bearings in each set are separated by exactly the width of the film. When the film passes between the bearings, the edges of the film ride on the bearings keeping the film exactly centred between them. The film is kept f l a t in the image plane by making use of the natural curvature of the film. The film i s inserted in the trans-port i n such a way that the curvature would cause the film to flex out of the image plane toward the photomultiplier or back side of the transport. A smoothly polished pressure plate i s then applied to this side of the film to keep i t in the image planed On the opposite side of the film to the pressure plate, the film rides on a set of r a i l s s i t -uated at each edge of the film. The purpose of using r a i l s on one side of the film i s to reduce the force required to advance the film by reducing the f r i c t i o n . The width of the transport body near the film aperture was dictated by space constraints imposed by the optics of the digitizing system. The transport was required to present ah image to a plane between two lenses which had to be closely spaced to obtain the de-sired optical focus. Two light tight shutters are provided as well as a base for subsequent mounting of solenoids to operate them. These shutters, i n combination with a light tight l i d , w i l l make i t possible to use unex-posed film in the transport for redisplaying time varying images. Only one shutter need be opened for redisplay purposes but both must be open to allow light to pass through while digitizing the image. The film is driven through the image plane by using a sproc-keted wheel, such is normally used in 35 mm projectors, to p u l l the Figure 2.1A photograph of 35 mm film transport - front view s p o t s c a n n e r VO Figure 2.IB photograph of 35 mm film transport - top view Figure 2.1C Photograph of image d i g i t i z i n g system - Layout Figure 2.ID Photograph of image digitizing system - Close-up 12 film. The film is then drawn from the source reel (marked with an 's' i n figure 2.1) which maintains back tension via a f r i c t i o n clutch; guided by two small spools into the image plane; and drawn across the film aperture onto the drive sprocket. From here the film i s loaded onto a take-up spool (marked with a 'D' in figure 2.1) driven by a slipping drive belt. The slipping belt drive allows for the change in diameter of the take-up spool as more film i s wound onto i t and i s also driven from the same drive shaft which drives the sprocket. The driving power for the system i s supplied by a small step-ping motor which advances exactly fifteen degrees per step. This step size i s further reduced through a high precision gear train to about 0.035 degrees rotation of the drive sprocket per step of the stepping motor. Since one f u l l frame advancement requires ninety degrees of rotation of the drive sprocket, about 2800 steps are needed 'to advance one f u l l frame. Note that this gives a possible interframe registration accuracy of about one tenth of a sampling interval when the image i s sampled on a 256 by 256 grid. After having b u i l t the apparatus as described above, a number of tests were performed to check for possible sources of error. It was discovered that the nature of the load on the motor was such that steps would be missed occasionally at some stepping rates and that the motor could only be started at low rates. It was also found that, once the motor had been started, the stepping rate could be advanced slowly to the maximum speed of the motor. To overcome this problem, a shaft encoder was b u i l t and mounted on the stepping motor. This encoder and the associated d i g i t a l hardware determines the direction and size of any motion of the motor and 13 increments or decrements a step counter accordingly. In t h i s way i t was po s s i b l e to use computer c o n t r o l to obtain proper f i l m advancement. The computer algorithm used f o r t h i s i s shown i n fig u r e 2.2. The step counter i s f i r s t cleared (N10C 47 i n s t r u c t i o n on the Supernova). Then a number of pulses equal to the number of steps to be advanced i s issued from the computer (N10P 47). At the completion of the sequency of pulses, the number i n the step counter i s read i n t o the computer (DIA -, 47; where - i s the desired accumulator) and compared to the desired number of steps. I f some steps have been missed, the step counter i s again cleared and the number of missed steps are reissued. This process continues u n t i l no steps have been missed. The second problem, that of having to s t a r t at a low stepping rate, i s r e a l l y a non-problem i f the amount of time taken to advance the f i l m .is .unimportant. ..However., it-was found-.that the time required at the highest stepping rate at which the motor would s t a r t was about 75 seconds per frame. This means that about s i x hours would be required f o r a three hundred frame f i l m segment. For t h i s reason, an a c c e l e r a t i o n function was programmed to accelerate the motor to optimum speed and then decelerate i t again near the end of the advance. The deceleration was added i n case overshoot was p o s s i b l e . With this ramping function the f i l m advance time f o r three hundred frames was reduced to 1.5 hours i" 2.3 Interframe Re g i s t r a t i o n Technique The mechanical system as i t has been described thus f a r i s more than adequate for obtaining the required interframe r e g i s t r a t i o n p r e c i s i o n between any two adjacent frames of a f i l m sequence. However, when the number of frames to be r e g i s t e r e d becomes l a r g e , small r e g i s -t r a t i o n errors which are i n s i g n i f i c a n t between two adjacent frames could 14 ENTER CLEAR STEP COUNTER ISSUE SEQUENCE OF N PULSES \ OBTAIN STEP COUNT S FROM COUNTER Figure 2 . 2 Flow diagram f o r f i l m advance algorithm 15 accumulate, r e s u l t i n g i n a large o v e r a l l m i s r e g i s t r a t i o n . For this reason i t i s desireable to have some references on the f i l m so that, when the cummulative err o r begins to grow, i t can be corrected. Conventional motion p i c t u r e equipment provides for frame r e -g i s t r a t i o n while f i l m i n g and p r o j e c t i n g motion p i c t u r e s . The references used are the perforations located along the edges of the f i l m which draw the f i l m through the camera on p r o j e c t o r . As shown i n fi g u r e 2.3, the motion p i c t u r e industry standards [20] require a very accurate l o c a t i o n of these perforations with respect to the f i l m image. The aperture s i z e of the f i l m transport was increased allow-in g the performations along one edge of the f i l m to be scanned by the f l y i n g spot scanner. A sketch of the output video waveform thus gener-ated i s shown by the s o l i d l i n e i n fi g u r e 2.4. The high l e v e l c orres-ponds "to "li'ght passing through -the perforations san'd the low l e v e l 'cor-responds to l i g h t passing through the f i l m . The spikes at the t r a n s i -tions can best be explained by l i g h t being scattered when s t r i k i n g the edges of the perforations. Based on the c h a r a c t e r i s t i c s of the output video waveform, a scheme was devised to determine the locat i o n s of the edges of the perforations, with a very low s e n s i t i v i t y to v a r i a t i o n s i n f l y i n g spot i n t e n s i t y and opacity of the f i l m . The algorithm used f i r s t determines the mean of the video waveform. Then a l l values of the waveform which are above the mean are set to a l o g i c a l high l e v e l and a l l those below the mean are set to a l o g i c a l low l e v e l , r e s u l t i n g i n the square wave-form shown by the dashed l i n e i n fi g u r e 2.4. Tr a n s i t i o n s from a l o g i c a l high l e v e l to a l o g i c a l low l e v e l and vice versa are taken to corres-pond to an edge of a p e r f o r a t i o n of the f i l m . • Hr-,0 - E 3 • • A -—If;— Projector^ Z aperture "li I n Film - 1-1— Dimensions A Ii C I) E V a R Indies 0.K25 ± 0.002 ' 0.(100 i 0.002 o.7:;s ±0.002 0.0155 0.022 0.021 0.0-111 Not > 0.005 • •F • • • J • • Millimeters 20.05 x 0.05 15.25 ±0.05 18.7-1 ±0.05 0.56 0.53 1.2-1 Not >0.I3 -Guided edge 'Travel 4 Projector " aperture • Image • A • I T n A. a B 1 n l rr i 77 • t I •o A' o o a • • D a D D 9 0 ' G Dimensions A Film width /} Perforation pilch C Perforation width D Perforation height E Edge to perforation F Width between perforations C Perforation skewncss /. 100 consecutive perforation pitch intervals R Radius of perforation fillet Incites 1.377 ±0.001 0.1870 ± 0.0005 0.1 100 ± 0.000} 0.07S0 ± 0.0004 0.079 O.'.i'.i'.l 0.001 KS.700 0.020 ± 0.002 ± 0.002 .max ± 0.015 ± 0.001 Millimeters 31.975 4.7.50 2.701 I.OH] 2.01 25.37 0.03 47-l.OK 0.5 1 ± 0.025 ± 0.013 ± 0.010 ± O.tllO ± 0.05 ± 0.05 max ± 0.38 ± 0.03 Figure 2.3 Standard dimensions f o r 35 mm motion picture f i l m 1 7 -r Y i f r r j» D i r e c t i o n o'f Scan Figure 2.4 Sketch of video waveform from s i n g l e scan along perforations of 35 mm f i l m and the associated decision waveform generated. 18 The algorithm described above was implemented d i g i t a l l y . Sampling was done at a much higher rate than that used to sample the film images in order to provide more than sufficient interframe regis-tration accuracy. Using this technique i t was found that the location of an edge could be determined repeatedly over a large range of film segments and with scanning intensities varying from saturation to very low levels. In order to process a section of film, the algorithm shown in figure 2.5 i s used. Before any processing takes place, the position of the f i r s t perforation is determined as an origin position. Subse-quent frames are then properly registered by aligning their perforations with the original perforation position. 2.A Automatic Intensity Control for the Flying Spot Scanner Once the interframe registration techniques described above had been developed, i t was possible to maintain uniform sampling in a l l three image dimensions. There only remained the problem of guaran-teeing consistent quantization with time. Sources of error in quantization could arise due to time drifts i n the electronic hardware. To determine the possible causes and the degree of d r i f t over time, experiments were conducted using a single frame placed i n the digitization aperture and then scanned repeatedly. Numerous parameters were measured for both short term and long term variations. The only variation detectable was in the intensity of the beam spot of the flying spot scanner. This variation was not detec-table on a frame by frame basis, but a gradual decrease of intensity, was measured. In 3.5 hours the intensity of the beam decreased from normal digitizing intensity to zero. 19 START LOCATE ORIGIN PERFORATION 1 PROCESS FRAME ADVANCE FILM JUST SHORT OF NEXT 'FRAME YES YES STOP LOCATE PERFORATION ADVANCE FILM SMALL AMOUNT yigure 2.5 Flow diagram for a general film processing algorithm. 20 The beam i n t e n s i t y i s d i r e c t l y measurable as a r e s u l t of the addition of the p e r f o r a t i o n detection scheme discussed e a r l i e r . Since the l o c a t i o n of the p e r f o r a t i o n i s known, i t i s possible to scan a number of points i n the p e r f o r a t i o n . The d i g i t i z e d values thus obtained can be averaged to obtain a measure-of the beam i n t e n s i t y . Having mea-sured the beam i n t e n s i t y , the computer can transmit a d i g i t a l number to a d i g i t a l / a n a l o g converter which controls the gain of the video a m p l i f i e r of the f l y i n g spot scanner. The computer algorithm used i n c o n t r o l l i n g the beam i n t e n s i t y i s shown i n f i g u r e 2.6. The computer hunts f o r a p r e s p e c i f i e d i n t e n s i t y , i n c r e a s i n g the beam i n t e n s i t y when i t i s too low and decreasing i t when i t i s too high. When the correct i n t e n s i t y i s found, the algorithm i s terminated. 2.5 The D i g i t i z e d Images The complete flow diagram f o r the image d i g i t i z i n g algorithm i s shown i n f i g u r e 2.7. This algorithm incorporates a l l the features of the system described previously i n t h i s chapter. A l l the subroutines w r i t t e n to implement the algorithm are included i n the appendix. Care must be exercised i f modifying c a l l s to routines since two d i f f e r -ent c a l l i n g sequences are employed. Some routines are w r i t t e n i n f o r -tran and c a l l s to and from them use the normal f o r t r a n c a l l i n g sequence i n Data General's DOS f o r t r a n package. Assembler routines c a l l e d from f o r t r a n routines and assembler routines c a l l i n g f o r t r a n routines w i l l use the DOS sequence for these c a l l s . However linkages between two assembler routines use normal accumulator parameter passing techniques and not the DOS sequence. These facts are w e l l documented i n the routines. For subsequent experimentation, d i g i t i z e d images c o n s i s t i n g Figure 2.6 Flow diagram of beam i n t e n s i t y c o n t r o l algorithm START ADJUST SPOT BRIGHTNESS AND LOCATE A TRAILING EDGE OF A PERFORATION DIGITIZE FRAME ADVANCE FILM JUST SHORT OF NEXT FRAME READJUST SPOT BRIGHTNESS YES YES - V ^ STOP LOCATE EDGE OF PERFORATION NO ADVANCE FILM SMALL AMOUNT FLOW DIAGRAM FOR DIGITIZATION OF 35 MM FILM Figure 2.7 Flow diagram for the Image Digitizing Algorithm 23 of 256 by 256 eight b i t samples per frame were stored on magnetic com-puter tape compatible with the IBM system. The information i s stored i n two eight b i t image samples per word with the f i r s t byte as the odd numbered sample and the second byte as the even numbered sample. These words are stored i n blocks of'2048 words corresponding to sixteen l i n e s from the image. Sixteen blocks therefore make up a s i n g l e frame. Successive frames are delimited by end of f i l e markers with an a d d i t i o n a l end of f i l e being placed before the f i r s t frame. No end of f i l e was placed a f t e r the l a s t frame but this can e a s i l y be added f o r s i t u a t i o n s where i t i s needed. 24 III. MODELS FOR IMAGES 3.1 Introduction In this chapter, we consider the question of s t a t i s t i c a l model-ling of monochromatic time varying images. A measure i s presented for comparing the validity of certain popular models which assume data stationarity. Non-stationary models would probably describe the phy-s c i a l phenomona more closely. However the criterion of analytic trac-t a b i l i t y makes the choice of a stationary model imperative. In section 3.2, the models of interest are presented and dis-cussed relative to their analytic trac t a b i l i t y . The a b i l i t y of these models to represent picture data i s then evaluated as a result of experiments on selected test data. 3.2 Stationary Models for Monochromatic Time Varying Images A monochromatic time varying image constitutes a three dimen-sional source which can be specified by i t s grey level u(x,y,t) at each of the coordinates (x,y,t) x^here x and y are the spacial coordinates and t i s the time coordinate. Sampling and quantization of the contin-uous source function u(x,y,t) generates a three dimensional f i e l d of discrete samples u'(x., y., t, ). An ensemble of such images can then be modelled by interpreting u'(x., y., t, ) as a random f i e l d . For simplicity i t can be assumed that the random f i e l d i s a zero mean f i e l d . This can always be made true by determining the mean intensity of the f i e l d and then subtracting this mean from every point i n the f i e l d . That is i f u = E {u'(x±, y , t^)} (3.2.1) 25 where E{*} denotes the expectation operator, then a zero mean f i e l d u ' (x., y., t ) can be generated as O 1 J K. u'o ( V y j ' V = u ' ( x i » Vy " (3.2.2) Previous experiments [7] have l e d to the adoption of the Gauss-Markov f i e l d as the most widely used model f o r p i c t o r i a l data. This stationary model i s s p e c i f i e d i n terms of i t s aut o c o r r e l a t i o n function as R(x, x \ y, y', t, t') = a 2{exp [-a | x-x' | -g |y-y' | - y | t - t ' | ]} (3.2.3) 2 where a i s the s i g n a l power of the source and a, g and y are parameters. Defining Ax = x - x', Ay = y - y', and At = t - t ' , (3.2.3) can be re w r i t -ten as R(Ax, Ay, At) = a {exp[-a|Ax|-g|Ay|-y|At|]} (3.2.4) •:;I-n ,what ..follows, -we normalize our experimental-data so ..that our i n t e r e s t l i e s with R q (Ax, Ay, At) = R ( A x > A y » A t > = e xp[-ajAx\-g|Ay|-y|At 1] (3.2.5) o The primary a t t r a c t i o n of the Gauss-Markov f i e l d model i s the a n a l y t i c s i m p l i c i t y a r i s i n g from the s e p a r a b i l i t y of the autocor-r e l a t i o n function i n t o a product of one dimensional au t o c o r r e l a t i o n functions R o(Ax, Ay, At) = Rx Q(Ax) Ry^(Ay) Rt^(At) (3.2.6) where RX q(AX) = exp [—cx[Ax| ] Ry Q(Ay) = exp [-g JAy|] Rt Q ( A t ) = exp [-Y| At) ] In a ddition, other a n a l y t i c s i m p l i f i c a t i o n s can r e s u l t from the condition that a l l s t a t i s t i c a l information about a sample point i s contained i n 26 adjacent previous samples. Consider what separability means for a f i e l d in two spacial dimensions R Q (AX, Ay) = Rxo (Ax) Ry Q (Ay) (3.2.7) Assume a homogeneous f i e l d where RX q (A) = Ry Q(A), then R O (A, A) = [Rx o(A)] 2 = [RyQ (A)] 2 or R q (A, A) = Rxo (2A) = RyQ (2A) From this i t can be seen that, for this model, the autocorrelation function decreases more rapidly with distance along the spacial diagonal than along either of the spacial axes. Intuitively this does not seem reasonable since the orientation of the spacial axes with respect •to the image i s somewhat arbitrary. "Also, previous work [15] has shown the correlation along the spacial diagonal as predicted by this model to be much lower than the value measured for experimental images. Kretzmer [1] also supports this arguement by finding that, i n general, there was no preferred direction of correlation for the images be studied. On the basis of these arguements, another stationary model in two spacial dimensions i s obvious with R Q (Ax, Ay) = exp [-(a 2(Ax) 2 + f32 ( A y ) 2 ) 1 / 2 ] (3.2.8) Note that for x = 0 or y = 0, this model reduces to the same form as the Gauss-Markov f i e l d . This means that the parameters a and 8 are identifiable with the a and 8 of the Gauss-Markov f i e l d model. Equation (3.2.8) may be extended to model a f i e l d generated by a time varying 27 image. In this case we have 9 9 2 2 2 9 1/2 R q (Ax, Ay, At) = exp [-(a (Ax) Z + 3 (Ay) + y ( A t ) Z ) X / Z ] (3.2.9) The remainder of this chapter is devoted.to the study of the models proposed by (3.2.5) and ( 3 . 2 . 9 ) . The objective i n what follows w i l l be to obtain a measure of the closeness of f i t of these models to selected experimental data. 3.3 Closeness of F i t Criterion One aim of this thesis i s to measure the accuracy of certain popular models in describing image data. In order to measure a model's closeness of f i t to experimental data, a meaningful measure must f i r s t be chosen. The measure chosen here is the mean squared error (MSE) between the experimentally determined autocorrelation function and the •model with ^parameters a, 3 attd'"Y'*"adjusted-vto 'provide 'a'least squares f i t to the experimental data. A more compact measure is defined by D = -10 l o g l 0 (MSE) (3.3.1) For example, i f the MSE is 0.0001, the measure i s D = 40.0. From (3.3.1) i t can be seen that D 1 > D 2 when MSE_L < MSE2. 3.-4 The Source Images Two professionally produced 35 millimeter film strips were chosen as subjects for experimental computations. Figure 3.1 contains selected frames from these strips together with the subject number and a number corresponding to the location of the frame in the film s t r i p . A large amount of degradation has occurred i n the reproduction of these pictures for this thesis. These subjects were chosen subjectively on the basis of the amount of spacial detail and type of motion exhibited. 28 subject o n e , frame 68 subject one, frame 84 F i g u r e 3.1A P h o t o g r a p h s o f s e l e c t e d f r a m e s f r o m e x p e r i m e n t a l i m a g e f i l m s t r i p s 29 Figure 3.IB Photographs of selected frames from experimental image film strips 30 subject two, frame 151 subject two, frame 168 Figure 3.1C Photographs of selected frames from experimental image film strips 31 subject two, frame 251 subject two, frame 268 Figure 3.ID Photographs of selected frames from experimental image film strips 32 subject two, frame 284 subject two, frame 300 \ Figure 3.IE Photographs of selected frames from experimental image film strips 3 3 It i s believed the subjects chosen represent a good variation on sub-ject detail and motion. Subject one and two consist- of 100 and 300 frames, respectively, at 25 frames per second. 3.4.1 Subject One Subject one was chosen primarily because of the specialized nature of the motion involved. Only a small portion of the image under-goes motion and this motion i s executed quite quickly. Also, for the f i r s t half of the sequence, this motion has a periodic acceleration and deceleration. The background, which remains stationary throughout the sequency of frames, consists of a stock exchange board on which a grid has been drawn containing names and numbers. The lettering and grid are of high spacial detail and the board spaces are of low spacial detail. Within"the " f i r s t ten-frames, a woman's head and shoulders pass i n front of the camera, but outside the depth of f i e l d and there-fore out of focus. This constitutes a low spacial detail, moving image. For the remainder of the sequence, very l i t t l e of the head remains v i s i b l e , while a hand erases some of the numbers on the board and then enters new numbers i n their place. The erasure of the board takes place from frame fifteen to frame fifty-one. This section i s temporally quite periodic in nature. Figure 3.2 demonstrates this periodicity. This diagram is a plot of the average temporal correlation at a time lag of one frame as measured along the sequence of frames. The rapid variation in the function of figure 3.2 from frame fifteen to frame fifty-one corresponds to rapid hand motion. The dashed line in figure 3.2 i s the mean temporal corre-lation at a time lag of one frame over the entire 100 frame film sequence. Figure 3.2 Average temporal c o r r e l a t i o n at a time lag of one frame versus the frame number of the f i r s t frame for subject one 35 3.4.2 Subject Two Subject two was chosen because of i t s wide variety of content. Both the background and the foreground vary with time and contain varying amounts of spacial detail. This image also contains varying rates of motion, from rapid to almost stationary. The background con-sists of both high detail and low detail areas and changes as the camera is panned to follow the foreground objects. The motion consists of two men walking up a set of stairs into the view of the camera which is mounted on the landing. Once the men reach the landing, one of them pauses as the other continues around him, at the edge of the film, and then proceeds up a second f l i g h t of stairs. Once the second man has passed out of view of the camera, there i s a period of very l i t t l e motion in which the man on the landing casually -surveys his^surroundings. This is --a-.segment -of very-high correlation, often reaching 0.99. The man on the landing then turns and rapidly mounts the second f l i g h t of stairs as the camera pans to follow him, thus completing the entire film sequence. Again these details can be observed i n figure 3.3 which i s a plot of the average temporal correlation at a lag of one as measured along the 300 frame film segment. The dashed line indicates the mean temporal correlation for a lag of one frame over the entire film segment. 3.5 Measured Model Accuracy To evaluate the models, the autocorrelation functions of the experimental film subjects were computed along five directions in the f i e l d ; a) along the x spacial axis Figure 3.3 Average temporal c o r r e l a t i o n at a l a g of one frame versus the frame number of the f i r s t frame for subject two. 37 b) along the y s p a c i a l axis c) along the s p a c i a l diagonal d) along the time (t) axis e) along the diagonal i n the x-t plane. Then exponential curves were f i t t e d , by a l e a s t squares f i t method, to each of the computed c o r r e l a t i o n functions to determine a, 3 and y f o r the models. The measurement of the autocorrelation functions was performed on the Supernova system while the l e a s t squares f i t was computed on the IBM 360/67 system. Defining the sample i n t e r v a l i n each of the x, y and t direc-tions of the f i e l d to be unity, the desired a u t o c o r r e l a t i o n functions could be computed by evaluating P(k) = •2 (L-k)MN 2 L(M-k)N L-k M N I I I u'0(£» m> n ) uV & + k> m> n) 1=1 m=l n=l f o r the x d i r e c t i o n L M-k N I I I U'Q(£' m» n> u'0<£' nrt"k» n> 1=1 m=l n=l (3.5.6a) (3.5.6b) fo r the y d i r e c t i o n L-k M-k N I I I u' («,, m, n) u' (5,+k, m+k, n) (3.5.6c) 2 (L-k)(M-k)N a %=L m—1 n=l fo r the s p a c i a l diagonal -T L K(N-k) \ \ T U'o a' m' n ) "'o^' n + k ) ( 3' 5' 6 d ) a 1=1 m=l n=l fo r the time d i r e c t i o n L-k M N-k I I ' I u f (£, m, n) u 1 (£+k, m, n+k) (3.5.6e) a 2 a-k)M(N-k) £ = 1 m = 1 n = 1 f o r the x-t diagonal 38 where ? L M N ° = l \ \ u'o ( £ ' m ) u'o m » n ) Jt=l m=l n=l (3.5.6f) and L = 256, M = 256, N = number of frames. However, about 64 hours of continuous computing time would be required to evaluate only a single point of one of these functions for the 100 frame subject. The same evaluation performed on the 300 frame subject would take 192 hours. If fifteen points on each function i n (3.5.6) were to be evaluated for both subjects, the total continuous computing time required would be about 192,000 hours or 800 days. Since this amount of computation time is prohibitive, i t was decided to compute the desired correlation data by averaging over a subset of the f i e l d . (3.5.6) was modified to reflect this fact with P(k) .1 , -1 L i M N 'I 1° I u' U", m:', ri) u' (£'+ k, m' , n) (3.5.7a) n2 L..M N L. i L ' o° 1 o £=1 m=l n=l for the x direction L M N T I I u ' <4'» m'» n) u ' m ' + k> n> (3.5.7b) 2 L M N 4=1 m=l n=l a o 1 < for the y direction L M N I I I u ' ( f . V . n ) u ' a ' + k , n ' + k . n ) 2 L MnN . . , . 0 1 1 H=l m=l n=l for the spacial diagonal (3.5.7c) 2 L M (N-k) . . , . a o o £=1 m=l n=l L M N-k i° r i u'„ *> u \ m'» n + k> for the time direction (3.5.7d) 2 L M (N-k) „ _ . cr 1 o SL-l m=l n=l L M N-k I 1° I u» (A\ m', n) u' (*' + k, m\ n + k) v. for the x-t diagonal (3.5.7e) 39 where 9 , L M N <? ~ T 1° l u' o ( r , mV, n) u' o (£', m', n) (3.5.7f) o o £.=1 m=l n=l V = 8£ - 7, m' = 8m - 7, L = 32, M = 32, L, = [1/8 (256-k)l , o o 1 1 1 and M.. = [~i/8 (256-k)l where denotes "nearest larger integer". The computing time required to evaluate (3.5.7) is about 300 hours; a much more r e a l i s t i c figure. Using (3.5.7), the autocorrelation function was computed along each of the five chosen directions for both subjects. This was repeated for the x direction for subject one using two other subsets of the f i e l d , yielding results which differed numerically by less than one percent, thus verifying the soundness of this modified approach. The computed autocorrelation functions for both subjects —along each of the directions chosen are-shown i n figure 3.4. The x's denote the computed correlation values and the solid line i s the least squares f i t to those points of an exponential. Remember that i n one dimension (3.2.5) and (3.2.9) reduce to the same function. The values of the exponents obtained by f i t t i n g exponentials to the data points are tabulated in table I along with the mean squared errors for these f i t t e d curves. It i s d i f f i c u l t to compare these results to those obtained by other researchers due to the differences in the picture data analyzed. The exponential coefficients are a function of spacial detail and degree of motion of the image, the sampling rate i n each of the dimensions, the number of levels of quantization, and the amount of noise i n the digitized image. However, these results are consistent i n form with previous results [1, 7, 9, 15]. 40 Figure 3.4A Correlation in x-direction for (a) subject one (b) subject two 41 Figure 3.4B Correlation in y-direction for (a) subject one (b) subject two 42 A3 ] .00 ~~i 1 r 8 10 12 X-Y DISPLACEMENT (a) r 14 T 16 18 i 20 44 1.00 -3 0.20 i r 8 10 12 X-T DISPLACEMENT (a) 14 16 18 20 Figure 3.4E Correlation in x-t direction for» (a) subject one (b) subject two 45 SUBJECT DIRECTION EXPONENT -10 l o g 1 ( ) (MSE) X 0.2297 20.63 Subject 1 y 0.1921 16.49 (100 frames) t . 0.0519 18.90 xy 0.3051 19.99 xt 0.3793 23.09 x 0.0551 25.89 y 0.1128 29.73 Subject 2 t 0.0610 20.57 (300 frames) xy 0.1171 29.67 xt 0.1024 21.46 TABLE I Exponential constants f o r l e a s t square f i t t e d curves and the r e s u l t i n g e r r o r measure. 46 As can be seen from figure 3.4, the computed autocorrelation function i n i t i a l l y f a l l s off more rapidly than the f i t t e d exponential model and less rapidly later. This d i f f i c u l t y in f i t t i n g an exponential to the data suggests that some model other than an exponential would be more accurate in describing p i c t o r i a l data. Having computed the autocorrelation function of the two sub-jects and f i t t e d exponential curves to the computed data, i t i s a straightforward matter to evaluate the proposed models as to the close-ness of f i t to the experimental data. Using the curves f i t t e d to the computed data along the x, y and t axes, together with the proposed models, the correlation along the x-y and x-t diagonals can be predicted. These predicted diagonal autocorrelation functions are given in figure 3.5 for both subjects along with the actual computed diagonal correla-tion functions.. .•:Meas-ui?.es-of..<0'loseness.*o.f. - f i t for ..the .two -models and both subjects are summarized in table I l a . From table I l a i t can be seen that the model which most closely matches the experiment data for both diagonal directions i s d i f f i c u l t to determine. What table I l a indicates i s that a compromise model between (3.2.5) and (3.2.9) is appropriate, namely R o (Ax, Ay, At) = exp [-(a 2(Ax ) 2 + 8 2(Ay) 2) 1 / 2- Y|At|] (3.5.8) Now defining Rt (At) = exp [-yI At I] (3.5.9a) o RX q (Ax) = exp [-a|Ax|] (3.5.9b) R y o (Ay) = exp [-g |Ay|] (3.5.9c) (3.5.8) can be rewritten as Ro(Ax, Ay, At) - Rt Q (At) exp [-([ln(Rx Q(Ax))] 2 + [In(Ry Q(Ay))] 2) 1 / 2] (3.5.10) 47 Figure 3.5A Predicted and computed diagonal a u t o c o r r e l a t i o n functions f o r (a) Subject one (b) Subject two x computed O separable model A nonseparable model ~I 1 1 r~ 6 8 10 12 X-T DISPLACEMENT (b) l 18 i 20 2 i 14 i IB Figure 3'.5B Predicted and computed diagonal a u t o c o r r e l a t i o n functions f o r (a) Subject one (b) subject two 49 ^Dimensions X.Model Subject \ Separable Non-Separable Subject 1 (100 frames) x, t 24.92 20.48 x, y 24.61 48.55 Subject 2 (300 frames) x, t 28.54 23.42 x, y 19.58 33.84 (a) \v _. . \""Model N. Dimensions N. Subject \ . Separable Non-Separable Subject 1 (100 frames) X, t 24.98 23.37 x, y 18.23 24.53 Subject 2 (300 frames) X, t 23.17 27.02 x, y 17.82 29.07 0 0 Table II Model accuracy (D = -10 log^Q ( M S E ) ) i n predicting diagonal autocorrelation based on (a) exponentially f i t t e d curves (b) computed data points 50 and s i m i l a r l y (3.2.9) and (3.2.5) can be rewritten, r e s p e c t i v e l y , as R q(AX, Ay, At) = exp [ - ( [ l n ( R x Q ( A x ) ) ] 2 + [ l n ( R y o ( A y ) ) ] 2 + [ l n ( R t o ( A t ) ) ] 2 ) 1 / 2 ] (3.5.11) R (Ax, Ay, At) = Rx (Ax) Ry (Ay) Rt (At) (3.5.12) o o o o Now consider s u b s t i t u t i n g the computed experimental values f o r the auto-c o r r e l a t i o n function f o r RX q(AX), Ry Q(Ay) and R t Q ( A t ) i n equations (3.5.10), (3.5.11) and (3.5.12). Through t h i s s u b s t i t u t i o n and computa-t i o n we determine whether or nat the experimentally measured autocor-r e l a t i o n takes a separable or non-separable form. Figure 3.6 i s a presentation of the diagonal a u t o c o r r e l a t i o n functions derived using (3.5.10), (3.5.11) and (3.5.12), and the e x p e r i -mentally computed autocorrelation along the two diagonal d i r e c t i o n s f o r both subjects. The corresponding closeness of f i t measures are given i n table l i b . From table l i b i t can be seen that the model which most c l o s e l y matches the experimental data f o r both diagonal d i r e c t i o n s i s given by (3.5.8). 51 J .00 -g 0 2 4 6 8 10 12 14 16 18 20 X - Y DISPLACEMENT 52 1.00 •* X - T DISPLACEMENT Figure 3.6B Predicted and computed diagonal functions for (a) sub-ject one (b) subject two 53 IV. THE DPCM COMMUNICATION SYSTEM 4.1 Introduction In this chapter, we determine the e f f e c t of the er r o r i n using the Gauss-Markov model to describe the source data on estimates of the system performance f o r a commonly used type of communication system. Due to the assumption of s t a t i o n a r i t y of the source data, the estimate of the system performance could be poor when the source data i s non-stationary. Estimates o f the performance of a DPCM communication system using both the optimum, l i n e a r , one-dimensional p r e d i c t o r and the optimum, l i n e a r , two-dimensional p r e d i c t o r are obtained f o r selected t e s t data. These estimates are then compared to the computed system performances for that source data. -<4. -2 "System -Model A general model f o r a DPCM communication system i s shown i n fig u r e 4.1. The input samples x^ are obtained by three dimensional low pass f i l t e r i n g , sampling, and f i n e quantization of a monochromatic time varying image u(x, y, t ) . Image sampling i s performed by r a s t e r scanning successive frames of the image as described i n Chapter 2. The bandwidth of the low pass f i l t e r i n g i s determined by the width of the spot and the optics of the scanning system. A l i n e a r p r e d i c t o r i s used to form a predicted value z^ which i s compared to the input sample x^. The e r r o r e^ i s then quantized, coded, and transmitted over the d i g i t a l channel. The predicted value i s determined from the previous samples v i a an equation of the form N = I a y , (4.2.1) j = l 3 3-D FILTER |-+( AND DIGITIZER ',t) X i ' . \ QUANTIZER DIGITAL / S i CHANNEL > L LINEAR PREDICTOR -f —>-L 1 3-D RECONSTRUC-\ X . 1 TOR x ( x , LINEAR <L , s, z i PREDICTOR Figure 4.1 A DPCM communication system 55 where y. = x. + q. and q. = S. - e., the quantization error [151. i l l 1 1 i In the receiver, a similar predicted value z. is added to the r l received signal to form the output sample x^ = + z^, where N (4.2.2) z. = T a. x. . 1 3=1 J ^ In what follows, we consider two situations: optimum, one-dimensional, linear prediction based on the immediately previous sample and optimum, two-dimensional, linear prediction based on the nearest three previous samples. By optimum linear prediction is meant the mini-2 mization of E{e^ } by the proper choice of the coefficients i n (4.2.1). If quantization error is negligible, this criterion i s equivalent to minimizing oe 2 = E {(x ± - z±)2} (4.2.3) where z^ i s now given by N (4.2.4) z. = T a . x. . 1 A J ^ 2 ae i s called the prediction error variance. A more convenient form of (4.2.3) for comparing system per-2 2 formance, results from normalizing by ax = E{x^ }; 2 E{(x, - z,) 2} ae l I // o c\ —5- = 5 (4.2.5) ax E1X±} Performance of these linear prediction schemes w i l l be measured in terms of the signal-to-noise ratio given by SNR = 10 l o g 1 ( J ( I (4.2.6) 56 4.3 Optimum Linear One-Dimensional Prediction If the input sequence x^ is generated from a one dimensional f i e l d , then the optimum linear predictor is found by finding the a which minimizes 2-ae 2 E{(x. - ax. x l - l ax 2 E{(x.) 2} = (1 - 2ap + a 2) (4.3.1) where E{x. x } .... P - 1 - ^ (4.3.2) E{x ± } ax It follows from (4.3.1) that the optimum prediction coefficient i s a = p , resulting in ( 4^ j = ( 1 ~ p 2 )- (4.3.3) \ ax Ain Although a = p yields an optimum linear predictor for one-dimensional sources, i t is of interest to determine the increase in prediction error variance resulting from using a non-optimum prediction coefficient. Let this non-optimum prediction coefficient be a' and l e t A = ct'-p represent the deviation from the optimum prediction coefficient. Then the increase i n prediction error variance can be shown to be \ ax /a = ct \ax /a = p 2 2 If |A | = 0.1, then the ratio (ae /ax ) changes by only 0.01. This implies that the prediction error variance is relatively insensitive to variations i n the prediction coefficient. This is also reflected i n the flatness 2 2 of the curves in figure 4.2 which plot (ae /ax ) versus the prediction 58 coefficient. Figure 4.3 is composed of curves relating the signal to noise ratio i n decibels to a for different values of p. Although the flatness of the curves varies with p , these curves exhibit relative insensitivity to A near the optimum a. The degradation in signal to noise ratio from the optimum can be shown to be am / a = p = 10 log [ 1 I p 2 ] (4.3.5) 1 - p + A J Figure 4.4 is a presentation of the plots of (4.3.5) for different values of p. We note that irrespective of p , the system performance i s not greatly effected by using prediction coefficients in a range near the otpimum value. For example, i f p i s 0.9, then picking a in the range 0.8 to 1.0 w i l l produce a degradation less than 0.23 decibels, or about five percent. A previous study [15] by K.Y. Chang has examined the change i n system performance when the correlation of the input data is varied while a is kept constant. However consideration should be made of the fact that the optimum performance also varies with p . Thus, where Chang shows a decrease of performance of 13% for a = 0.89 and p decreased to 0.8, the optimum performance would also have decreased. From figure 4.4 i t can be seen that the degradation from optimum performance i s 0.12 decibels or about three percent. Two sets of experiments were performed to verify the general nature of the curves shown in figures 4.2 and 4.3. As can be seen from 59 09 61 these f i g u r e s , there i s good correspondence between the experimentally determined r e s u l t s and the a n a l y t i c r e s u l t s . For the points i n d i c a t e d by an x i n figures 4.2 and 4.3, the one-dimensional data was generated from subject one using successive points i n the x - d i r e c t i o n . The measured c o r r e l a t i o n between adjacent samples for this data i s 0.9188. For the points i n these figures i n d i -cated by a A, the one-dimensional data was generated from subject one using successive points i n the time d i r e c t i o n . The measured c o r r e l a t i o n between adjacent samples for t h i s data i s 017501. 4.4 The Optimum Linear Two-Dimensional P r e d i c t o r For an input sequence to the DPCM communication system con-s i s t i n g of samples from a two-dimensional f i e l d f ( i , j ) , we consider the optimum l i n e a r p r e d i c t o r using the nearest three previous samples to p r e d i c t the next sample. The c o e f f i c i e n t s i n t h i s p r e d i c t o r ; a^, a^, a.j» are chosen to minimize 2 E { [ f ( i , j ) - a - f d - l . j ) - a 9 f ( i , j - l ) - a_f (i-1,j-1) ] 2} £ e - = ± ^ — ± (4.4.1) of E { [ f ( i ^ j ) ] / } E { f ( i , j ) f ( i - l , j ) } D efining p- = r , (4.4.2a) E { - f ( i , j ) n E { f ( i , j ) f ( i , j - l ) } p , (4.4.2b) E { ' f ( i , j ) n E { f ( i , j ) f ( i - l , j - l ) } and p_ = ~ , (4.4.2c) J E { [ f ( i , j ) ] 2 } (4.3.1) can be rewritten i n the form 62 2 OQ. 2 2 2 ~ 2 = 1 + °1 + a2 + a3 " 2 a l P l " 2 a2 P2 " 2 a 3 P 3 + 2 a l a 2 P 3 + 2 a l a 3 P 2 + af 2a 2a 3 P ; L (4.4.3) It follows that the a^, and which minimize (4.4.3) are given by P l 3 " p l ~ P 1 P 2 2 + 2 p 2 p 3 " p l p 3 2 n , , ^  „ = - (4.4.4a) 1 P 2 , P 2 + P 2 . 9P P P . 1 1 2 3 1 2 3 P 2 2 - P 2 - P X 2 P 2 + 2 P l P 3 - p 2 p 3 2 a 2 = 2 2 2 (4.4.4b) P l + p2 + p3 ~ 2 p l p 2 P 3 " 1 p 3 2 ~ P3 ~ p l 2 p3 + 2 P1 P2 - p 2 2 p 3 . . , a 3 = ^ - - (4.4.4c) P1 + p 2 + p 3 - 2 p l P 2 P 3 - 1 Continuation of the analysis at this point becomes unweildly. For this -reason,, a .further.^restriction..is usually placed on .the model; the autocorrelation function i s assumed separable. With this added restriction, p 3 = P^P2 (4.4.4) reduce to a l = p l ; a2 = P 2 ; a3 = ~ P l p 2 (4-4.5) Substituting into equation (4.4.3), the minimized ratio becomes 2 (4.4.6) Now, as in section 4.2.1, we consider the effect on performance of the DPCM system when any or a l l of the prediction coefficients; a^, and a 3; are not the optimum coefficients. Let the non-optimum coefficients be a'^, a' 2 and a' 3 and define A^ = a'^ - p^, A 2 = a' 2 - p 2 > A 3 = a' 3 + p^P 2 as the deviation from the optimal coefficients. The increase i n prediction error variance i s given by 63 2 oe_ £2 O f if * '(ct^opt. = A x 2 + A / + A 3 2 + 2 P l A 2 A 3 + 2 p 1 A 1 A 3 + 2 p 1 P 2 A 1 A 2 (4.4.7) Note that i f any two of the deviations are zero, this equation reduces to (4.3.4). However, we also note that the performance is much more sensitive to changes of the coefficients. For example, i f p^ = p 2 = 0.9, ct^ and ct2 are the optimum prediction coefficients, and i s varied i n the range 0.8 to 1.0, then the system performance w i l l be degraded as. much as 28 percent. 4.5 Signal-to-Noise Ratio Measurement The signal-to-noise ratio for various DPCM communication systems was measured for subjects one and two using the Supernova mini-computer. In so doing, the mean values were f i r s t subtracted from the digitized samples representing the subjects. Refering to figure 4.5 which shows the seven nearest previous samples to sample X^ q, the prediction schemes tested were. 1. Previous Sample (x^) 2. Previous frame (x._) i3 3. Nearest three previous samples in x-y plane ( x i l , x i 2 ' x i 4 ) 4. Nearest three previous samples in x-t plane v X . ^ s X . « , X , / . J . i i i3 16 I t has been shown [15] that, for a Gauss-Markov f i e l d , the nearest neighbor prediction schemes we are looking at are optimum for a l l schemes using as many previous elements as desired for prediction. 64 • x i 4 ^ _ 4 x ± 2 1 > 1 - 1 / i f x . l . ' i 5 1 i M 1 X X i l x ' X i O 1 1 " 1 / " fr g-' x i 6 x i 3 Figure 4.5 Nearest previous samples for X^ Q used for p r e d i c t i o n to For schemes one and two, the signal-to-noise ratio was deter-mined by f i r s t measuring the prediction error variance N I i=l CTe2 = f  ( x i o " a x i i ) 2 ' (4.5.1) where N i s the number of image samples involved and r x., for scheme one l l x = < • x^3 for scheme two The optimum prediction coefficient results when a = p, where p i s the average correlation between two adjacent samples. We can determine p either by direct measurement or from the exponential model, i.e. p = exp [e] (4.5.2) where e = ct for scheme one and e = y for scheme two. Measurements were made using both prediction coefficients based on the measurement of p and on (4.5.2). The signal-to-noise ratio was then determined for each case as SNR = -10 l o g 1 ( ) (cre 2/a 2) , (4.5.3) where °2=l X ( x i o > 2 ' ( 4 ' 5 - A ) The results of these measurements are summarized i n table III. Included i n the table i s the predicted signal-to-noise ratio computed as SNRp = -10 l o g 1 0 (1 - p 2 ) , (4.5.5) where p again takes on a value obtained from direct measurement and from (4.5.2). Comparison of the results for schemes one and two leads to the following conclusion: the correspondence between predicted and measured \ v >v \Prediction N. N ^ S cheme Subject p o e 2 \ ^ scheme 1 ( x n ) scheme 2 (x ± 2) s cheme 3 ( x i l ' X i 2 ' X i A ) scheme A ( x i l ' x i 3 ' Subject 1 (100 frames) from (A.5.2) predicted ., 4.34 10.04 9.28 1A.38 measured 6.66 3.08 9.51 12.8A measured predicted 8.10 3.59 10.01 11.69 measured 7.AO 3.34 10.55 1A.01 Subject 2 (100 frames) from (A.5.2) predicted 9.87 9.39 16.80 19.26 measured 13.5A 4.41 16.88 1A.65 measured predicted 14.20 4.62 18.09 18.82 measured 13.89 4.61 17.01 15.26 TABLE III Signal-to-noise ratios predicted .from model and measured for DPCM communication systems operating on subjects one and two 67 signal-to-noise ratios i s poor when the least-squares f i t t e d model is used to determine p, whereas the correspondence i s very good when the measured correlation i s used. The poor correspondence can be attributed to the poor f i t of the exponential model to the f i r s t point of the auto-correlation function curve (see figure 3.4). From (4.5.5) i t can be seen that a small variation in p w i l l cause a large change in predicted signal to noise ratio. The system performance, however, i s improved only sl i g h t l y be using the measured correlation coefficient instead of the model correlation coefficient. This i s consistent with the analysis of section 4.3. For prediction schemes three and four, the experimental pre-diction error variance can be determined by evaluating N 2 ae "1 X ( x i o " °-l x i l " ll x i k " °3 X i * ) 2 ' ( 4 ' 5 ' 6 ) where x.. xk i=l { x_^ 2 for scheme three x^2 for scheme four ha = f x i 4 for scheme three x., for scheme four x6 Assuming a separable autocorrelation function, the optimum predictor results when ct^ = p^, = and = " P j ^ w ^ e r e P]_ 1 S t n e correlation coefficient i n the x-direction and i s the correlation coefficient in the y-direction for scheme three and the time direction for scheme four. As before, p^ and c a n be obtained both by direct measurement and from the exponential model. The measured signal-to-noise ratios are summarized in table III as well, along with the predicted signal-to-noise ratio determined as 68 SNRp = -10 l o g l 0 (1 - P X 2 ) (1 - P 2 2) (A.5.7) It can be seen that losing the measured correlation coefficients again produces the best results. There is good correspondence between the predicted and measured signal-to-noise ratios even when the correlation coefficients were determined from least-squares f i t t e d models. This is due to a compensating effect. As can be seen from figure 3.4, the measured x-direction correlation coefficient i s greater than the value determined from the exponential model whereas this situation i s reversed for both the y-direction and the time-dimension correlation coefficients. This means that the f i r s t term in parenthesis in (4.5.7) i s decreased while the second term is increased, producing the mentioned compensa-tion . Theoretical analysis based on the model of a Gauss-Markov f i e l d shows that the signal to noise ratio for two-dimensional predic-tion i s the sum of the signal-to-noise ratios for one-dimensional pre-diction i n each of the two orthogonal directions. From table III i t can be seen that, for subject one, the measured signal to noise ratio for two-dimensional prediction was about three decibels greater than the sum of the measured signal-to-noise ratios for one-dimensional predic-tions i n the two orthogonal directions. For subject two this two-dimensional prediction performance was three decibels lower than the sum. This i s probably due to the fact that the Gauss-Markov model poorly describes subject two i n the x-t plane, whereas the model more closely matches the data for subject one. Table IV i s a summary of the exponential coefficients and the measured correlations for the two subjects used in this study. SUBJECT DIRECTION e P X 0.2297 0.9188 Subject 1 y 0.1921 0.5960 (100 frames) t 0.0519 0.7501 xy 0.3051 0.5587 xt 0.3793 0.6891 X 0.0551 0.9811 y 0.1128 0.8516 Subject 2 t 0.0610 0.8094 (300 frames) xy 0.1171 0.8498 xt 0.1024 0.7955 TABLE IV Summary of exponential c o e f f i c i e n t s and measured c o r r e l a t i o n s used i n t h i s study 70 V. CONCLUSION. The results obtained from this study are summarized in the following. 1) A system has been designed and implemented which can be used to process and study time varying images 2) Using this system, experimental images have been digitized and stored on computer magnetic tape. This data has been stored on tape compatable with that used by larger computing installations making this data relatively installation independent and therefore readily avail-able for further experiments. 3) Statistics for two motion picture images were computed and compared to the sta t i s t i c s predicted by previously used models. For these images i t was found that an autocorrelation function of the form R(Ax, Ay, At) = exp [-(a 2 (Ax) 2 + B 2 ( A y ) 2 ) 1 / 2 - y\lit\] was most accurate i n predicting these s t a t i s t i c s . 4) The effect of using the Gauss-Markov model in designing a DPCM communication system on the performance of that system i n terms of signal-to-noise ratio was determined by comparing predicted,and measured signal-to-noise ratios for the system. 71 APPENDIX Supernova Support Routines for Digitizer The following subroutines are used to digitize picture images located on 35 mm film using the system described in Chapter Two. In order to use them, the main routine SCANN must be called from a fortran routine as follows: CALL SCANN STOP END This w i l l ensure porper i n i t i a l i z a t i o n of the fortran calling sequence. A l l subroutines are compiled and assembled separately. Once the above fortran program has been compiled and assembled, i t must be loaded along with the following subroutines and the fortran library FORT.LB using the supernova relocatable loader. 72 0©O1 ftCOUT ; T H I S S U B R O U T I N E I S D E S I G N E D TC OUTPUT ; A 16 B IT WORD IN OCTAL FORMAT OM THE TT ; A C 2 MUST C O N T A I N THE WORD TO EE . D I S P L A Y E D . T I T L ACOUT . ENT ACOUT . NREL " -0 0 0 8 C 0 5 4 4 3 7 ACOUT: ST A 3 , RTN i SAVE RETURN 6 8 8 8 1 ' S 4 6 4 3 7 ST A 8 , A C 6 SAVE A C S 6 8 8 3 2 ' 844." 37 ST A 1, AC1 0 6 Q 3 3 ' 0 5 3 4 3 7 ST A 2.. AC2 0 6 0 0 4 ' 8 2 8 4 3 7 • LI) A 8 , CS 0 0 0 3 5 • ' 8 4 8 4 3 7 ST A e, RPI 6 8 3 6 6 ' 8 2 8 4 37 LDA 8/ C68 ; F I R S T B I T 9 8 0 8 7 ' 1 5 1 1 2 2 i'lOVZL 2 , 2 , S Z C 0 0 8 1 8 ' 1 8 1 4 0 8 INC 8, 6 8 0 0 1 1 ' 6 8 0 4 1 1 JMP ENTER 8 S 8 1 2 ' 8 2 8 4 3 4 L 0 0 P 1 : L JJ A CO C3 :• SUBSEQUENT THREE 8 6 3 1 3 ' 6 4 8 4 3 4 STA 8 , R P 2 } GROUPS 8 8 8 1 4 ' 8 2 8 4 2 7 LDA 3 , CS 6 0 0 1 5 ' 1 0 1 1 2 8 LOOP2: i' lGVZL 8.- O EACH B I T 8 8 8 1 6 ' 1 5 1 1 2 2 M8VZL 2 , 2, SZC 0 6 0 1 7 ' 1 0 1 4 8 8 INC e, o 8 8 8 2 8 ' 3 1 4 4 2 7 DSZ R P 2 0 6 0 2 1 ' 8 8 8 7 7 4 JMP L 0 0 P 2 6 8 6 2 2 ' 0 8 6 8 3 2 E N T E R : . S Y S T M ; OUTPUT CHARACTER 8 8 8 2 3 ' 6 7 8 0 0 0 . P C H A R 0 6 8 2 4 ' 8 8 8 4 8 8 JMP " S " 8 8 2 5 ' 0 1 4 4 1 7 'DSZ ::.?\ 0 6 9 2 6 ' 8 8 8 7 6 4 JMP L O O P ! ' ' ' 0 0 8 2 7 ' 8 2 8 4 2 1 LB A 8 , C 4 6 ; BLANK CHARACTER 8 8 8 3 0 ' 8 8 5 6 8 2 . S Y S T M 0 0 8 3 1 ' 8 7 6 0 8 0 . P C H A R 0 0 6 3 2 ' 8 8 8 4 8 8 JMP 6 6 8 3 3 ' 6 2 6 4 0 5 LDA 6 . -AC6 ; R E S T O R E A C ' S 6 6 6 3 4 ' 8 2 4 4 6 5 LDA 1, AC1 6 6 8 3 5 ' 8 3 8 4 8 5 LDA 2 , AC2 6 6 3 3 6 ' 0 8 2 4 8 1 JMP 6RTN ; RETURN ; CONSTANTS 6 0 8 3 7 ' 8 8 0 0 0 3 RTN: 0 -6 6 0 4 8 ' 8 8 8 8 6 8 AC 8: 8 6 6 8 4 1 ' 0 8 8 0 8 8 A C 1 : 0 . 0 6 6 4 2 ' 8 8 3 6 8 6 AC2 : • 3 6 6 6 4 3 ' 8 8 0 8 0 5 C S : 6 8 6 8 4 4 ' 8 8 6 8 8 3 RP1 : 8 8 6 8 4 5 ' 8 0 8 8 6 8 C 6 6 : 68 6 6 6 4 6 ' 8 8 6 8 0 3 C 3 : 0 8 6 4 7 ' 6 8 8 6 8 8 R P 2 : e 0805O' 8 8 8 3 4 8 C 4 3 : 48 . END 73 0601 BRGHT ; THIS SUBROUTINE HAS BEEN DESIGNED TO ; ADJUST THE BRIGHTNESS OF THE SPOT OH THE FLYING SPOT SCANNER ; A CALL TO. ERSTA SETS THE INDEX ; LOCATION OF THE PERFORATION (EDGED :• INTNS IS CALLED TO DETERMINE THE ;- SPOT BR1GHNESS IN THE PERFORATION ; LOCATED BY 'EBGE1' . TITL BRGHT . ENT BRGHT,BR STA . EXTN. INTNS . EXTD . FCAL . NREL 0 S 0 0 0 ' 6 4 0 4 5 2 BRSTA: STA 8,EDGE1 STORE EDGE1 00001'OO1400 JMP 8, 3 i RETURN 0O0O2'640443 BRGHT: STA 8, AC0 SAVE AC'S 06O03'844443 STA 1, AC1 00004'050443 STA 2, AC2 00005'05443" STA 3, RTH SAVE RETURN 000G5'O2844£ LB A 8, C346 ; DESIRED INTENSITY = 346 0 0 0 0 7 ' o e e s e i ^ L O O P : JSR @.FCAL .; 0BT ft IN ACTUAL INTENSIT Y - 0 0 0 ] 0 ' i '77 77 7 -TNT-HS 00O1 1'008802 ~i O0012'0UO852 EDGE! 00O13'800851 • BRT 60014"024435 LDA 1s BRT . 00015'125220 MOVZR i , 1 ; DIVIDE BY 8 00016'125228 H0V2R 1* 1 00017'125228 riOVZR 1,1 00820' 186415. SUB » 0, 1, SNR ; EQUAL ?• 00021'080417 JMP ' DONE ; YES O0022'186513 SUBLft 0 , 1, SNC ; TOO LOU ? 00023'880418 JHP HIGH ; HO 00824'824427 LDA 1,BRT1 :• DECREASE CONTROL 0QO25'124408 NEG 1, 1 00026'125400 INC 1, 1 00827'124488 NEG \> 1 08038'044423 STA 1,BRT 1 00031'666045 DOB 1,45 ISSUE. CONTROL 08032'860755 JMP LOOP 00033'824428 HIGH: LDA 1,BRT 1 i INCREASE CONTROL 0 0 0 3 4 ' 125483 INC 1, 1 88035'04 4416 STA 1, BRT 1 0O836'866845 DOB 1, 45 ISSUE CONTROL 08837'888758 JMP LOOP 80048'828485 DONE: LDA 0, AC0 RESTORE AC'S 8O041'024485 LDA 1, AC1 00O42'8384S5 LDA 2, AC2 80043'0O240i JMP 0RTH RETURN 8 6 0 2 BRGHT i CONSTANTS 8 3 0 4 4 ' 8 8 3 8 8 8 0 8 8 4 5 ' 0 8 3 8 8 0 8 8 0 4 6 ' 8 8 8 8 3 8 0 0 0 4 ? ' 0 8 0 8 8 0 0 8 0 5 3 ' 0 0 0 3 - 1 6 0 8 8 5 1 ' 8 0 0 8 8 8 8 0 8 5 2 ' 8 8 8 3 8 8 6 0 8 5 3 ' 8 0 8 6 8 8 H: T i l : 0 A C 8 : 0 8 8 1 : 6 AC 2 : 8 L ' 3 4 6 : 3 4 6 F:RT: 8 :>:..\: 0 R R T 1 : 6 8 8 END 75 0 0 6 1 CUI.-IRT T H I S SUBROUTINE HAS BEEN D E S I G N E D TO ; CHECK THE S T A T U S OF A WRITE COHMAUD HAVING S E E N I S S U E D TO THE MAGNETIC ; T A P E U N I T . ; A L L ERRORS ARE CHECKED AND WHERE •  P O S S I B L E , REWRITE A T T E M P T S ARE MADE. • - A G O MUST CONTAIN THE ADDRESS OF THE > B U F F E R TO BE WRITTEN - AC1 MUST C O N T A I N THE WORD S I Z E OF ; THE B U F F E R . T I T L CHWRT . ENT CHWRT . EXTN C S K I P , A C O U T , W R I T E . . H R E L 8 8 8 8 8 1 . TXTM J. 0 0 0 8 0 0 4 8 4 6 1 " C H W R T : STA 8 , A C 8 SAVE A C ' S . 0 0 0 0 1 644 461 STA 1, A C i 0 0 0 8 2 6 5 8 4 6 1 STA 2 , AC2 0 0 0 0 3 8 5 4 4 6 1 STA 3 , RTH S A V E RETURN 0 O 0 8 4 1524C0 SUB —1 •-! iL..' i l l . i R E T R 'i' E R A S E = 0 0 0 8 0 5 8 5 8 4 6 8 STA 2 , R E T R Y 0 0 0 8 6 ' 8 5 8 4 6 8 STA 2 , E R A S E 0 0 0 0 7 ' 8 6 3 6 2 2 WAIT: SKPDN HTA :• WAIT FOR T A P E 0 0 0 1 8 ' 8 0 8 7 7 7 JMP . -1 .0001 1 : 0 7 8 4 2 2 V-l A 2 , HTfi •;• .0BTA14! STATUS 0 0 0 1 2 ' 8 6 8 2 2 2 NTUC MTA . 0 0 0 1 3 ' 8 6 8 1 7 7 I NT EN 80014 - ' 1 5 1 1 1 2 HOVLfi 2 , 2 , S Z C ERROR? 0 0 0 1 5 ' 3 8 8 4 8 5 JMP . +5 .;• Y E S 0 0 O 1. 6 ' 8 2 8 4 4 3 LDA 8 , A C 0 :• HO - RESTORE A C ©8017 • 8 2 4 4 4 3 LDA 1, AC 1 0 0 0 2 0 ' 8 3 0 4 4 3 LDA 2 A C 2 0 0 0 2 1 ' 8 8 2 ^ 4 3 JMP 0RTH ; RETURN 8 0 0 2 2 ' G 2 8 4 3 3 LDA 8, C 1 5 OUTPUT STATUS 8 0 0 2 3 ' 8 6 6 8 0 2 . S Y S T M 0O024 ' 0 7 8 0 8 8 . P C H A R 0 8 0 2 5 • S 0 0 4 8 8 JMP 0 0 O 2 6 • 8 2 8 4 2 6 LDA 8 , C 1 2 0 0 O 2 7 ' 0 0 6 8 8 2 . S Y S T M 0 0 0 3 0 ' 0 7 0 80O . P C H A R 8 8 0 3 1 ' 0 0 8 4 8 3 JMP 8 0 0 3 2 ' 886 4 26 JSR. 0AOUT 8 0 0 3 3 • 1 5 1 1 2 0 MOVZL cl.' 0 0 0 3 4 ' 1 5 1 1 2 2 MOVZL 2, 2, SZC .: DATA L A T E ? 8 8 0 3 5 ' 0 8 8 4 6 4 JMP L A T E Y E S 0 0 0 3 6 ' 1 5 3 1 2 2 ADDZL 2 , 2 , SZC .: I L L E G A L ? 0 0 0 3 7 ' 6 6 8 5 1 5 JMP L E G A L • Y E S 0 0 0 4 8 ' 1 5 3 1 2 2 ADDZL 2, 2, S Z C • P A R I T Y ERROR? 0 0 0 4 1 " 8 0 8 4 6 0 JMP L A T E ; Y E S 0 0 O 4 2 ' 1 5 1 1 2 2 MOVZL 2 , 2 , S Z C END OF T A P E ? 8 0 8 4 3 ' 0 C 8 5 2 1 JMP EOT .: Y E S 0O84 4 ' 1 5 1 3 0 0 MOVS -"I ; ODD C H A R A C T E R S ? 0 8 0 4 5 ' 1 5 1 2 2 2 MOVZR • 2 , 2 , S Z C : 76 8 O 0 2 CHMRT 6 0 0 4 6 •' 0 6 0 4 5 3 0804',' ' J 2 0 4 2 8 © 0 6 5 6 ' 0 6 4 0 7 S T O P : 0 8 O 5 1 ' 0 0 6 0 8 2 • O 0 0 5 2 ' 8 0 4 4 8 8 O O 8 5 7 ' 8 O 8 4 8 U 08054'008812 C12: 00855''OG8015 C15: 88856'808 185 OF: 00057' 177777 AURT: 88868' 177777 ACUT: 0 8 8 6 1 ' 0 0 0 0 8 0 0 0 8 : 0 0 8 6 2 ' 8 8 8 8 0 8 AC1: 0 8 8 6 3 ' 8 8 8 8 8 8 AC2: 8 8 0 6 4 ' 8 8 0 8 8 8 RTH : 00O65 •' 868808 RETRY 80066'086808 ERASE 00067' 090378'' T X T I : 88070'886412 80071'852516 86072'645516-80073'647527 00074'847O40 80075'052101 00076'058185 08877'826127 80 i 0 0 ' 0511 11 :081-01 ' 05-21-05 80162'820105 001 03'651122 08104'847522 801O5'028055 80106-' O26105 0O1O7'054103 08110'041525 001 1 1 •' 0521 1 1 001 12'04 7516 60113'820124 801 1 4-' 042522 00115'646511 801 16'647181 00117'652165 80128'642880 60121'024427 LATE: 00122-' 068277 80123'067822 00124'838425 08125-' 071 122 00126'063622 06127'006777 O013O'860222 00131'O60177 08132'S20733 00133'824417 J M P L A T E ; . Y E S L D A O, TI:'!! :• U N K N O W N E R R O R J S R e^WRT . S Y S T M . R T N J M P . . . ; C O N S T A N T S 1 5 1 6 5 W R I T E A C CUT 0 3 8 3 6 0 . +1. . T X T ,'-< 1 5 > < 1 2 ; U N K N O W N T A P E W R I T E E R R O R - E X E C L H LDA, 1,CM1 ; SPACE REVERSE INTDS DOC 1, NT A LDA 2, C4 0 DC AS 2, MTA SKPBH MTA ; WAIT FOR TAPE JMP . -1 NIOC MTA INTEN LDA 0,RETRY ; RETRY = 8? LDA ;, ClO 6803 CHiVP"!' 0 0 I 3 -. ' ! 0 6 4 0 3 OO i 35 ' OOO-'?;: Q 8 1 3 6 ' 1014 00 00137' 340726 00140 ' 060237 P. I T C : 00141'020730 0!O142' 062022 OO 1 43'020717 00144'053022 60145'020406 08146'861122 03147'880640 SUB 0, 1 , S H R JMP RUB .i YES IMC @,0 J R E T R Y «= R E T R Y + 1 ST A V I, RETRY IHTI'S L.Tun 3, AC8 DOB O,MTA LDA 8, ftf: 1 DOC 8,-MTA LDA 8,058 DOAS 0,MTA JMP WAIT ; CONSTANTS 08150 177777 CMl : -• 1 08151 008840 C4 8: 4 8 08152 088613 C 3 8 : 10 60153 868650' 058: 58 80154 628445 LEGttL: LDA 8, TXT2 08155 606702 JSR 8AWRT 88156 826470 CANC: LDA 6, TXT3 0O157 824448 LDA 1, Al 88168'886436 JSR 0ASK IP 80161 886602 .SYSTM 80162 864483 . RTN 09 16? 800400 JMP .: TAPE PILE PROTECTED 66164'326513 EOT: LDA 0, T X T 4 i END OF TAPE 8 316 5'0 OS 6 72 JSR !* tt W RT 86166'008770 JMP CANC . 0016? '023677 RUB: LDA 6,ERASE J ERASE - 8? 00178 '024762 LDA 1, C1 3 08171 '186404 SUB O, 1, SZR 06172 ' 0 8 3 4 6 3 JMP . +3 ; NO • 0O173 '82053? LDA 0/TXT5 ; YES 60174 '088654 JMP STOP O0175 '181400 I HO- O, O i ERASE = ERASE + 80176 '848676 ST A O,ERASE 0O177 '182433 SUB O, 0 ; RETRY • = 0 00236 1648665 ST A O,RETRY 08201 '868277 1NTDS 00262 '823416 LDA 0,C70 ; ERASE A SECTION 0O263 •861122 DO AS O, MTA 80264 '063622 SKPDH MTA ; WA I T FOR TAPE 00285 '668777 JMP . -1 06286'028742 LDA 8,CMl ; SPACE REVERSE O02O7 '863022 DOC O, MTA 06218'828741 LDA 8, 04 0 6021 1 •861122 DO AS 6, MTA 00212 •063622 SKPDN MTA i WAIT FOR TAPE 00213 • 30677? ..IMP 08214 '66017? 1NTEN 00215 '086723 JMP RITE ; CONSTANTS 78 8 8 9 4 CHURT 0 0 2 ; C ' • n S (•,'IP •. 0 0 2 1 7 ' 0 0 0 1 4 u ' A ' : • 0 0 2 2 0 ' 0 0 0 0 7 0 C 7 0 : 8 0 2 2 1 ' 0 0 0 2 2 2 ' T X T 2 : O S K I F P I T H 78 . +1 . TXT •< 1 5 X 1 2 > T A P E P I L E P R O T E C T E D - CORRECT 0 0 2 2 2 ' 0 0 0 4 1 2 8 8 2 2 3 ' 0 2 0 0 4 0 ' ' 0 0 2 2 4 ' 0 2 8 0 4 0 0 8 2 2 3 ' 8 5 2 1 0 1 • -0 8 2 2 3 ' 0 5 8 ! 8 5 8 8 2 2 7 ' 8 2 8 1 0 4 8 8 2 3 3 ' 0 4 4 5 : 4 8 8 2 3 1 ' 0 4 2 4 4 8 83232'053122 8 8 2 3 3 ' 0 4 7 5 2 4 8 8 2 3 4 ' 8 4 2 5 0 3 8 0 2 3 5 ' O 5 2 1 8 5 0 0 2 3 b ' 3 4 2 8 4 G 0 8 2 3 7 - ' 0 204 4 0 8 0 2 4 O ' 8 4 1 5 1 7 0 8 2 4 1 ' 0 5 1 1 2 2 0 3 2 4 2 ' 0 4 2 5 0 3 8 0 2 4 3 ' 6 5 2 8 4 3 8 8 2 4 4 ' 0 4 0 5 1 6 8 8 2 4 5 ' 8 4 2 8 8 8 8 0 2 4 6 ' 0 8 8 2 4 7 ' T X T 3 : .+1 . T X T - ' < 1 5 X 1 2 > T Y P E ' Y ' TO CONTINUE OR ' N ' 1 0 0 2 4 ? ' 8 8 6 4 1 2 0 O 2 5 O ' 3 2 8 0 4 0 8 0 2 5 1 ' 0 2 8 8 4 8 6 8 2 5 2 ' 8 5 2 1 31 0 8 2 5 3 ' 0 5 8 1 8 5 . • 8 8 2 5 4 ' 8 2 8 0 4 ? 8 0 2 5 3 ' 0 5 4 4 4? 8 8 2 5 6 ' 8 2 0 1 2 4 0 O 2 5 7 ' 8 4 7 4 4 8 88268'e41517 0 O 2 6 1 ' 8 4 7 1 2 4 ; O 8 2 6 2 ' 0 4 4 5 1 6 0 0 2 6 3 ' 0 5 2 5 8 5 0 8 2 6 4 ' 8 2 8 1 1 7 0 0 2 6 5 ' 0 5 1 8 4 8 O 0 2 6 6 ' 8 2 3 5 1 6 0 O 2 6 7 ' 8 2 3 4 4 0 8 O 2 7 0 ' 0 5 2 1 1 7 6 0 2 7 1 ' O 2 0 1 1 0 0 O 2 7 2 ' 0 4 8 5 1 4 0 8 2 7 3 ' 8 5 2 0 4 8 0 O 2 7 4 ' 0 2 8 0 4 8 O 0 2 7 5 ' 8 2 8 8 4 0 0 0 2 7 6 ' 0 O 0 O 8 8 0 O 2 7 7 ' 8 0 0 3 6 0 ' T X T 4 : .+1 . T X T ,' < 1 5 > < 12 > END 0F T A P E E NC 0(JNTERED - M0 L G O 3 0 0 ' 6 8 6 4 1 2 0 O 3 6 1 ' 0 2 8 0 4 0 O O 3 3 2 ' 0 2 0 0 4 O 0 3 3 8 3 - 3 4 2 5 1 6 0 0 3 0 4 ' O 4 2 0 4 O 0 8 3 8 5 ' 0 4 7 5 0 6 . ' 79 o s e s CHURT 00396'020124 0030?'040020 00310'042440 .00311'042316 80312'641517 08313'852516 09314'0521S5 08315'051105 083 IS'042048 00317'026440 0O32O'04651? 00321'852516 00322'852040 06323'04?165 08324'053^48 00323'032101 08326-1 056105 08327'828181 0O336'047104 00331-006888 00332'088333'TXT5: .+1 TXT •-'< 15 >< 12 >UHABLE TO UR-1TE OH TAPE - EXECUTI 88333'886412 0O334'O52516 06335'848582 80336'846185 98337'028124 06348'84744 8 86341'853522 O0342'044524 06-343'^!42440 00344'0475! 6 O0345'820124 80346'846526 80347'842448 0O358'826446 09351'642530 • 00352'842583 ' 08353'652524 86354'84451? 88355'847848 08356'852165 00357'851115 80360'044516 80361'040524 60362'842584 08363'800688 END 80 9001 CSKIP THIS SUBROUTINE HAS BEEN DESIGNED TO BUILD A S K I P CHAIN VIA USER INTERROGATION ACQ MUST CONTAIN A POINTER TO THE DESIRED PROMPT A C i MUST CONTAIN A JUMP LOCATION TO BE TAKEN ON AN A F F I R M A T I V E RESPONSE .THIS MAY BE A SUBROUTINE ADDRESS AS THE RETURN FOR THIS ROUTINE WILL BE PASSED ON . T I TL C S K I P . ENT C S K I P . EKTN WRITE . NREL 000O0-' 054434 CSKIP: ST A * 3, RTN ;SAVE RETURN 00001'640434 ST A 6, AC© :• SAVE AC'S 00062-' 044434 ST A 1.. ACI 000O3'656434 ST A 2, A C 2 0 0 6 0 4 ' 6 0 6 4 3 6 AGAIN: JSR 8 AWRT WRITE MESSAGE O8005'606602 .SYSTM i GET RESPONSE 0O066'667460 .GCHAR 080 O 7 ' 8 0 6 4 0 8 JMP 0 0 6 1 6 ' 6 3 6 G 0 2 .SYSTM 8 6 0 1 1 ' 0 7 6 0 0 8 .PCHAR 06612'880436 JMP 0O013'824425 LDA 1, CY A F F I R M A T I V E ? •0004-4'1064-04 -•SUB - - c a v e s z R 88815'838406 JMP NO 8 0 0 1 6 ' 6 2 8 4 1 7 LDA 8.. A CO ; RESTORE AC'S 88817'024417 LDA 1, ACI 80826'838417 LDA 2, AC2 0 O 0 2 1 ' 0 3 4 4 1 3 LDA 3, RTN OO022'602414 JMP 0AC1 JUMP OUT 8 8 8 2 3 ' 8 2 4 4 1 6 NO: LDA 1,'CN i NEGATIVE? 80O24'136484 SUB 8, 1, SZR 88825'600465 JMP • BACK 8 8 8 2 6 ' 8 2 0 4 6 7 LDA O, ACQ RESTORE AC'S 88027'0244O7 LDA 1, ACI 8 8 0 3 8 ' 6 3 6 4 S 7 LDA 2, AC2 88031'882483 JMP 0RTN ; RETURN 88032'826463 BACK: LDA O.. ACO 80033'668751 JMP AGAIN ; CONSTANTS 0 0 8 3 4 ' 6 0 6 0 6 8 RTN: 8 8 0 0 3 5 ' 8 8 6 0 8 8 AC8: 8 8O036'886883 ACI : 8 86037'0863S8 A C 2 : 0 O004e'088131 CY: 131 88041'883116 CN: 116 88042'177777 AWRT: WRITE END 81 C THIS SUBROUTINE HAS BEEN DESIGNED TO DETECT C THE LOCATION OF THE EDGE OF A PERFORATION C OF THE 35 HH F ILPi C C CALLED FROM ASSEMBLER BY: C JSR O.FCAL C EDGE C - 2 C EDGE1 • C EDGED - -C C WHERE EDCE1 IS THE INDEX LOCATION C AND EDGED IS THE DIFFERENCE BETWEEN '• C THE INDEX AND ACTUAL LOCATIONS C C IF EDGE 1 IS ZERO ON CALL TO THIS ROUTINE C EDGE i WILL BE SET EQUAL TO THE PRESENT C LOCHFI ON OF THE EDGE C C IF EDGE! IS NONZERO , EDGED WILL BE SET C EQUAL TO THE DIFFERENCE BETWEEN EDGE I AND C THE PRESENT LOCATION OF THE PERFORATION C EDGE C C DUE TO THE DYNAMIC STORAGE ALLOCATION USED C BY THE SUPERNOVA FORTRAN , VARIABLES DO NOT C RETAIN THEIR VALUES FOR SUBSEQUENT CALLS TO C A ROUTINE . FOR THIS REASON THE PARAMETER C EDGE 1 MUST BE STORED IH AN ASSEMBLER ROUTINE C OR IN THE COMMON AREA . C C . L-INE-P "IS • CALLED''TO 'SCAN THE T-'ET*F ORATIONS C FILLING THE ARRAY IYB C C C SUBROUTINE EDGE <IEDG1,IEDGD> DIMENSION I YB '. 1 024 ) C • • C *** SCAN PERFORATIONS C CALL L I HEP <IYB> C C *** SET THREASHOLD AT MEAN C S=0.8 DO 2O0 1=1,1024 S=S+IYB<: I 2 0 0 CONTINUE ITHR=S / - i024 . 08 C C *** FIND LEADING EDGE OF PERFORATION C DO 201 1 = 1, 1024 J = I I F C IYBCI>. GE. ITHR> GO TO. 2 6 2 2 0 1 CONTINUE STOP 'NO EDGE FOUND' C C +*+ FIND T R A I L I N G EDGE OF PERFORATION C c 2 0 2 DO 203 I = J , 1024 82 IF '•. I Y B < I > . L T . I 7 H R > GO TO £64 203 CONTIt-lUE STOP -,N0 EUC-E FOUND' 204 IF OEDGt . if. 8. 0) GO TO 205 C C *** SET EDGED TO DIFFERENCE C • IEDCD = J-It"D01 RETURN C C *** SET EDGE1 TO EDGE LOCATION C 205 IEDGI=J RETURN • 83 6001 FIKOS ; THIS S I ! Br; OUT I H E HAS B E E N DESIGNED TO ; POSITION 35 MM FILM . TI TL F1P0S . ENT FIPOS . E x 7 N GTNUU,ro i IV, Fill-OS, WRITE. C S i a p , FMSTA, EDO . lOOOi . FCAL . HO-El.. 803831 . TXTM 1 60603 ' 030 : '30 F IPOS.- ST A 3, RTN' SAVE RETURN 63001 •' 64 3400 ST A 0, AOO i SAVE AC'S 63 0' 0 : 4 430 • S;A 1, ACI 8 0 3 8 3 •' 333406 ST A ?... AC2 8 3 8 0 4 •' 0 2 8 0 0 S 1.0 0 0, T/iTl :• MESSAGE - FILM POSIT I OKIHG 001885•' 000006 • JSR 8AWRT 08303' UP MORE: L Ii A 0, I ft 1 2 .; MESSAGE- ENTER COMMAND 3:":SO'.'•' 8334 3 0 JSR 6AWRT 680 j. 3' 023533 L I: A o, IXTS ; MESSAGE- COMMAND CHOICES 888:1 1 •  63S174 JSR OfiWRT 0661 3'' 636^70 JSR OAGNUM GET COMMAND 808 33'162406 SUA 0, 0 CHECK FOR BAD 60314'1424 05 SUB 2, O, SNR RESPONSE 6 8 3 1 5 ' 8 0 S 7 7 1 JMP MORE BAD- REINITIALIZE 608 IS'626435 LB A 0, 03 CHECK FOR BAD 83817'112513 SUSLvt 0.. 2, SNC RESPONSE 00023'00 0700 JMP MORE 0S621'853451 ST A Z, CMND STORE IT 06622'020053 F 'I POL: L P A O, TX f 4 MESSAGE- HOW MANY TIMES? 06023'03S4S2 JSR 0AWRT . 00824'3SS45S JSR PAGHUM GET RESPONSE 80325' 182408 SUB 6, O CHECK FOR BAD 6302:.' 142405 SUB 2, 6 , SNR RESPONSE O0S27'663773 JMP FIP01 BAD- REINITIALIZE 03830'633445 ST A 2, TIMES STORE IT 00331'03C441 LDA 2/CHND GET COMMAND 6633:4' 628442 LDA 0, C2 .IS IT A 80333'112415 SUBtt 6, 2, SNR TWO? 00334'663434 JMP MOVE YES- GO TO MOVE 06035'024 44 0 LDA 1,TIMES NO-CALL STEP ROUTINE 60830'636446 JSR OAF ST 60337'000423 JMP END GO TO END 00648'1S2433 HOVE : SUB O, 8 SET FLAG TO FIND EDGE! 60041'04S43? ST A 0, EDGE 1 633 42'80644 6 JSR • OABRST SET EDGE1 TO ADJUST INTENSITY 0S343'606446 JSR 0A8R r A D J U S T B RIG H T H E S S 60344.' 863001 ? JSR 0 .FCAL CALL EDGE 00:345' 17777? EDGE 00346'633832 O0647'030163 EH 0171 03858'83307? DUM 60351'62642? LDA 8,EDGE 1 ; SET EDGE 1 IN FMPOS 03352'806427 JSR OAFSTA 00853'806435 JSR G'ASP: ST ; SET EDGE! IN BRGHT 00051'00642? n o v E i : " JSR 0ATM20 ; CALL FRAME ROUTINE 08055'8144 00 D S Z TIMES ENOUGH FRAMES? 86056'600/76 JMP • MOVE i ; NO- CONTINUE 088:--: FIFOS 8:\ ••.?•• s;-; •: : : rt-il): C366;' 885;/3 (;•;_• 1 628 " :. 60368;-:-; 35 0 9 >:•';>' 03248: 0006 6'8038 f i 8 0 7 •' 8 £ 3 8 ; ! •"'1 \ ' f; 6!"' -~J U ! -' 8808 S 3 3 7 2' 3 3 8 0 80873' 8068 98•-'.'.••(' 6683 0 8 3 7 5' 6 3 8 8 63876'8331 8 S 8 7 7 •' 8 6 8 6 03 1 . 8 3 8 8 88181'17?? CD 1 8;-:' 17 77 C0183'17?? OO' 0-'.' 1??? 80105'1777 0C186'1777 88137'8388 861 16'1777 081!1'177? SOI 12' 8681 i.3' VXTi : 88113'0864 12 881 l ' l ' 026048 081 15'02O84R 8P116'043111 881 17'0461 i 5 08126'028120 60121'847583 60 122'O'i4524 8!3123'044517 88124'3471 1 1 09125'0471 67 08126'886838 03127'688136'TXT2-00130'8864!2 88131'826843 GUI 32'8288-13 83133'023O4O 88134'826S48 80133-042516 80136'852135 3813V'651043 88146'84 i 517 0G14 1.'646515 88142' 0485 3 6 &8143'C42i25 I ME i\:.;f.:T: i.nn 0 , T>: r 5 L'Jrt t.AMORF J8R CASK IP Li? A 8, ft CO LL'A ' , 1:6 1 L3iA •?., AC2 ; MESSAGE- AMY I i 0 R ? • RESTORE AC'S ;. RETURN ;CONSTANTS 3 0 8 8 8 0 116 • 0 3 FMSTA G T N 3 M FAROS FSlEP WRITE C8KIP MORE E-RS"i A ERG NT . +1 . TXT /< 1 5 >'< 12> FILM P0SIT10N IHG TXT /<15><1?> ENTER COMnAM 85 m<t>3 K i r o s 88 I 4 4' 6388'; 0 8,', 1 V ' 6861 40 ' THTS: 03:43' ess -1-1:? 00 14?' 626843 00130' C283' ;: 0 00151'32604 0 so 152'3253 :4 00153'038450 86 i34'05]511 CO 155 ' 84716? 00 136' 6 4 C H 1 5 00157'028 j 23 68168'652185 68161'65685• 60162'88684 3 63167'331356 03164'65151J 331.65' 64716? 831 66'846185 63 167'688 i 36 88173'851161 68171'846585 0O172'324443 63173'835846 88174'623888 861 75' 3861 75 ' TXT4 .• + 1 TXT /<15;<12> 1(SINGLE STEP 2(SINGLE + 1 TXT /<i5><12> HOW MANY TIMES? 83176' 88177' 88283'. 83231' 83282' 88263' 88264' 83205' 08236' 88267' 00216' 88211' 08212' 00213' 00214' 08215' 88216' 08217' 88228' 08221' 80222' 86223' 86224' 00225' 8854 12 828343 6 2 0 5 4.5 82 00':8 82834 8 0441 1? 633443 846501 8475 31 326124 04 45I 5 642523 037443 02834 8 02884 8 02884P 033843 62004 3 8283 4 0 82334 3 828348 82884 6 838866 868326 • TXT5 . TXT ,'<15><3 2> POSITIONING ECHE. ANY HOP 86326'8364!2 08227'028643 88238'6' -040 08231'08684 3 88232'828040 08235'053i17 86 ©oc-4 r IPOS 003.34' 351.51 I O'-:: •:.;' o : :. •' ' so;- - v i ' o4:• : : •<•". o •;>;! • s i v C ' i ' ' 35< •'; 3 3 3:-: 4 3' 3433 ' 3 632 7 •' 05 j 135 p 5 i ' 328043 E H Ii 87 8001 n-i p os j. THIS SUBROUTINE HAS BEEN DESIGNED TO i ADVANCE 35 MM FILM BY ONE FRAME ; A CALL TO FMSTA SETS EDGE1 TO THE :• INDEX LOCATION OF THE EDGE OF THE : PERFORATIONS :• A CALL TO FMPOS ADVANCES THE FILM ; .TO THE NEXT F RAME ; FSTEP IS CALLED TO ADD CORRECTION ; STEPS DUE TO POSITIONAL ERROR ; EDGE IS CALLED TO DETERMINE THE ERROR * IN POSITION BY DETECTING THE LOCATION .; OF THE PERFORATIONS AND COMPARING THI ; TO THE INDEX LOCATION ' EDGE 1 '' ; BRGHT IS CALLED TO ADJUST THE ; BRIGHTNESS OF THE FLYING SPOT SCANNER .TITL FMPOS . ENT FMPOS,FN STA . EXTN FSTEP,EDGE, BRGHT . EXTD . FCAL . NREL 00008' 04651.0 .FMSTA: ..STA EDGE1 00001 • 001400 JMP 8, 3 00002'054518 FMPOS: STA 3, RTH :• SAVE RETURN 88083'' 048518 STA 0, AC© ; SAVE AC'S 08064'844518 STA 1, AC1 00005'050518 STA 2, AC2 68006'828475 LDA 8/C12 • ; OUTPUT AN 'S' 86887'886882 .SYSTM ; • TO DENOTE END OF 08810'070600 .PCHAR 68011'868488 JMP 88012'828472 LDA 0, CIS 88313'606032 .SYSTM 88814'076668 .PCHAR 80815'0O040O JMP 80016'020467 LDA 8, CS 80017'086082 .SYSTM 88820'070680 .PCHAR 88921'886480 JMP 80022'628474 LDA 8, AC NT i PUT PULSE 80023'824474 LDA 1,ADELY i COUNT AND 80O24'638474 LDA 2, C l ; DELAY ADDRESSES 88025'142488 SUB- 2, 8 ; IN AUTO-O0026'14 6406 SUB 2, 1 INCREMENT 08027'040823 STA 0, 23 ; LOCATIONS 88038'044824 STA 1, 24 88031"023471 LDA 8,COUNT ; INITIALIZE 08832'848471 STA 8,TCNT COUNTER 88833'624472 LDA 1,DELAY i INITIALIZE DELAY 88 6062 FHPOS 00O3-: •  06824 0O835 00836 0O837 'O0048 08041 80842 08043 00044 00045 08846 0004' 80058 00051 80052 00053 822823 G-3466 022824 86834 7 186488 844464 014463 088777 814457 380772 014454 800765 860447 024435 186468 L00P1: LOOP; 00O54'' 1251 13 80655' 88056' 80057-666434 O86061$LOOP3: 177777 00060'' 80861' 00862'' 08863' 86864' 80O65' 88866' 00O67' 86070' 88671' 86872' "88073' 08074' O0075' 00676' •80877' 8O100' 08101' 88182 088802 888118' 866121' 824436 .1251 12 388465 125865 886463 686421 880765 326414 036682 078688 688466 886411 626414 824414 03O414 80241O BONE: HI 00 LDA 01 ri L D A HI OP SUB ST A B3Z JMP DSZ JMP HSZ JMP BI A LDA SUB MOVLtt JSR JSR EDGE 4 7 to 023 0, PONT 0. . 024 47 6.. 1 1, DELA DEL A • -.1 P 0 NT L00P2 TO NT L00P1 0, 4? 1, NPLS 0, 1 1, 1, SNC OAF ST 0.FCAL INITIALIZE PULSE COUNT AND DELAY ISSUE PULSE INITIALIZE DELAY DELAY LOOP LAST PULSE? NO- CONTINUE FRAME IN POSITION? NO- CONTINUE OBTAIN PULSE COUNT ANY ERRORS ? YES - GO CORRECT DETERMINE EDGE DIFFERENCE EDGE1 EDGED LDA 1, EDGED .; NEGATIVE ? MOVLS 1,1, SZC JMP DONE j YES MOV i , 1, SNR ; ZERO ? JMP DONE i YES JSR OAFST ; GO CORRECT JMP L00P3 .; RECHECK POSITION LDA G, CM ; OUTPUT AN 'M' ."SYSTM i TO 'DENOTE * .PCHAR JMP JSR OABRT ADJUST BRIGHTHES LDA O, A C O i RESTORE AC'S LDA 1, ACI LDA 2,-AC2 JMP' ORTH i RETURN ; CONSTANTS 88183'000812 012: 12 88184'060015 C15: 15 80105'060123 CS: 123 80106'880115 CM: 115 80107'17777? ABRT: BRGHT 00110'880006 EDGE1: 0 801 1 1-' 177777 AFST: FSTEP 00112'080000 RTN: 8 881 13'000088 AC8 : O 881 14'886688 ACI.- 8 00115'000880 AC2: 3 08116'800136'ACNT: C NT 88117'880151'ODELY: DLAY 80126'860001 C l : 1 08121'068008 EDGED: 8 OO122'880021 COUNT: 21 00123•300080 TCNTs 0 0003 fMPOS 60124 '638880 PC NT: 8 O0125 '644636 DELAY : 4 4631 06126 ' 600068 i'ELA: 0 0612? '685360 NPLS: 5366 08136 '680824 ONT: 24 68131 '6868 56 50 00132 '600074 74 0O133 '030121 i 2 1 00134 '066151 151 00135 '800225 t: i.\ 0O136 '808256 250 00133 y y y 1*  * i r* 7 4'7 06148 '601138 1130 08141 '686727 727 00142 '688258 250 80143 '366225 £25 88144'868151 151 86145 '800121 121 88146 '866674 74 60147 '006850 50 80158 '068624 24 00151 '668727 DLAY: 727 80152 '8661 16 116 00153 '868832 - 32 00154'006814 14 ©0155 '806086 6 00156'003063 00157 '888802 08168 '888861 1 00161 '686060 O •80.1,62 '.1.77777 -1 80163'177776 -2 00164'177775 -3 00165 ' 177772 -6 00166 ' 177764 -14 00167 '177746 -32 00176'177662 -116 00171 '177051 -727 END 90 0061 FSTEP T I T L ENT NREL T H I S SUBROUTINE HAS BEEN DESIGNED TO ADVANCE THE 35 MM TRANSPORT BY A GIVE i NUMBER OF S T E P S THE D E S I R E D NUMBER MUST BE IH A C i FSTEP FSTEP 00800' 654432 FSTEP: STH 3, RTH • SAVE RETURN 860C.1 •' 040432 STA 0, ACO SAVE AC'S 0O082• 644432 ' STA 1, AC 3 00003' 353432 STA 2, ACS 03004 • 125112 LOOPA: HOVLff i .< 1, Si z c NEGATIVE ? 00865' 888463 JMP DONE ; YES 00006' 125364 MOV 1, 1, SZR ; ZERO ? 00007' 800465 JMP START ; HO 00016' 020423 DONE: LDA 0, ACO ; RESTORE AC'S 00011' 324423 LDA 1, ACI 80812' 636423 LDA 2.. ACE 888?3' 832417 JMP 0RTN RETURN 80014' 644422 START: S T A 1,STEPS 00015' 124466 NEG 1, 1 i NEG.' STEP COUNTER 00016' 66824? NIOC 47 ; CLEAR COUNTER 80017' 863347 LOOP: HI OP 47 ) ISSUE PULSE 00823-' 62641 7 LDA 3,DELAY 80021'100488 NEG O, O 06822' 161464 INC O, 0, SZR j DELAY LOOP O0023' 80077? JMP . -1 8O024' 125404 INC 1,1, SZR :• ENOUGH PULSES? 88025' 068772 JMP LOOP i NO 80026'O60447 DI A O, 47 i OBTAIN COUNT O0027' 624467 LDA 1,STEPS 00030' 186468 SUB 8, 1 i NEW COUNT 00031' 880753 JMP LOOPA i BACK AGAIN i CONSTANTS 00032'000009 RTH: 0 80633' 888863 ACO : 0 008 34' 886868 ACI : 0 8O035' 663888 A 32 : 0 00036'000060 STEPS: 0 88837' 840800 DELAY: 4 00O8 END 91 0001 GTNUM ; THIS SUBROUTINE HAS BEEN DESIGNED TO ; ACCEPT MULTIPLE DIGIT DECIMAL ; INTEGERS FROM TT AND CONVERT TO .; BINARY INTEGERS ; INPUT LS TERMINATED ON RECOGNITION ; OF A NON-NUMERIC CHARACTER i 'ANY NUMBER LARGER THAN IS BINARY i BITS WILL BE TRUNCATED OH THE ; LEFT ; NEGATIVE NUMBERS HAVE NOT BEEN CONSIDERS • .RETURNS THE BINARY VALUE IN ACS .TITL* GTNUM . ENT GTNUM . NREL 00006 •  054 436 GTNUM: STA 3, RTN SAVE RETURN 80001 •' 840432 STA 8, A68 88032'644432. STA 1, ACI 80863'858432 STA 2, ACS 80084'' 1524S0 SUB 2.. S 808O5-806O82 LOOP: .SYSTM * OBTAIN DIGIT 00806'057488 .GCHAR 88887'888460 JMP 88818'886662 .SYSTM 888,1.1 '.07SS68 ..-PCHAR 88812'006489 JMP 86613'824424 LDA 1, C57 ABOVE LOWER LIMIT? 88814' 1065 5.3 SUBLtf 6, 1, SNC 88815'08S413 JMP DONE .• HO-RETURN 80016'624422 LDA 1, C7S ; BELOW UPPER LIMIT? 88817'122513 SUBL'S 1, O, SNC 88828'860410 JMP DONE . i HO-RETURN 88021'824420 LDA 1 / C 1 7 i ASCII TO BINARY 88822'123408 AND 1 .. 0 88823'145086 MOV 2, 1 i MULTIPLY NUMBER BY 08824'153128 ADDZL •-. •-, LI, > u_ 88825'133128 ADDZL 1, 2 60826'113868 ADD O, 2 i ADD DIGIT 88027'666756 JMP LOOP 00036'82O403 DONE: LDA 0, ACO 00031'824483 LDA 1, AC 1 88832'8O2404 JMP 0RTN ) CONSTANTS 88833'680880 A C 0 : 0 88834'608060 ACI : 0 88035'000886 A C 2 : 8 88836'008068 RTN: 3 88837'80O057 C57: 57 88040'608072 C72: i £. 88841'080817 C17: i? END 92 C THIS SUBROUTINE HAS BEEN DESIGNED.TO C DETERMINE THE OVERAGE SPOT BRIGHTNESS 0 OR THE FLYING SPOT SCANNER C C LINEP IS CALLED TO SCAN THE PERFORATIONS C C THE INTENSITY IS MEASURE!) THROUGH THE C FILM PERFORATIONS WHEN POSSIBLE C C IF THERE IS INSUFFICIENT PERFORATION • C AVAILABLE TO MAKE THIS MEASUREMENT , C THE INTENSITY IS MEASURED ON THE C FIRST 40 POINTS OF THE PERFORATION SCAN C C THE METHOD. USED WILL BE INVARIANT C TROUGHOUT THE SET OF FRAMES SCANNED C SINCE THE PERFORATION IS MOVED TO C THE SAME LOCATION WI TH EVERY ADVANCE C C CALLED FROM ASSEMBLER BY : C JSR -CECAL C INTNS C 2 C IEDG1 C I NTS C C WHERE EDGE1 IS THE INDEX LOCATION . C OF THE FILM PERFORATION AND INTNS C IS THE AVERAGE INTENSITY MEASURED C C DUE TO THE DYNAMIC STORAGE ALLOCATION C USED BY THE SUPERNOVA FORTRAN , 'C VAR-I'ABi^ES 'DO NOT 'RETAIN - THEI-R VALUES C FOR SUBSEQUENT SUBROUTINE .CALLS . C THEREFORE I EDS 3. MUST BE STORED IN C AN ASSEMBLER ROUTINE OR IH THE COMMON C AREA C C C : SUBRCUTI HE IHTNS < I EDG 1, I NTS > DIMENSION IYB<1024> CALL LINEP (IYB) S = 8. 0 J= IEDG1-68 K=IEDGl-20 C IF <J.LE.0> GO TO 281 C DO 288 I=J,K S-S+IYBO) 200 CONTINUE r GO TO 203 C 281 DO 202 1=1,41 S = S+IYBU) 282 CONTINUE C 203 INTS = S.--40. 68 RETURN END 93 0 8 0 1 L I HEP ; T H I S S U B R 0 U l I HE HAS P E EN DESIGNED T0 ; SCAN 1 HE P E R F O R A T I O N S ON THE F I L M :• T H I S IS A P A R T I A L SCAN OF THE F I L M ; WIDTH AT A VERY HIGH D E N S I T Y i i C A L L E D -BY = i C A L L L I HEP <ARRAY) ; . WHERE ARRAY = B U F F E R FOR SCAN I i R E Q U I R E S HARDWARE I N T E F A C E ON D E V I C E . T I T L L I HEP . E N T L I N E P . E X T D . C P Y L , . F R E T . NREL 17761 1 X = - 1 6 7 0 0 0 8 6 1 FS.=1 6 0 0 8 0 - ' 0 8 8 0 8 1 F S . 0 0 0 8 1 •' 8 8 6 3 8 1 S L I N E P : J S R 0 . C P Y L ENTRY 8 8 8 8 2 - ' 8 2 8 4 3 2 LDA 8 . C 3 7 7 ? 0O063- ' 6 6 2 8 4 4 DOB 0, 44 ; I S S U E '.' TO SCANNER 06864 - ' 863644 DOC 8, 44 ; I S S U E Z TO SCANNER 0 0 8 6 5 •' 83561 1 LDA 3 , X, 3 B U F F E R ADDRESS 8 8 0 8 6 - 8 2 8 4 3 1 LDA 63 P P L IN ; I N I T I A L I Z E POINT 6 8 6 8 7 - ' 1 8 0 4 8 8 NEG 0 , O COUNTER 0 0 0 1 6 - ' 6 2 4 4 2 5 LDA 1 , C 7 7 7 ; X-COORD I NATE 0 0 0 1 1 •' 8 4 4 4 2 5 ST A 1, XX 0O012- ' 06O277 INTDS 0 6 6 1 3 - ' 0 6 5 1 4 4 CONT: DO AS 1, 44 ; I S S U E X COORDINATE 0 0 0 1 4 ^ 8 6 3 6 4 4 SKPDN 44 :• WAIT FOR OOO 15-' 0O8777 JMP . -1 .; SCANNER OOO 16-' 8 7 3 4 4 4 IU A 2, 44 O B T A I N V A L U E 0 0 0 1 ? ' 6 2 4 4 1 5 LDA 1 / C 3 7 7 7 MASK TO 0 6 0 2 3 - ' 1 3 3 4 8 6 AND !., 2 ; E L E V E N B I T S 6 0 8 2 1 •' 0 5 1 4 8 6 STA Z, O, 3 STORE V A L U E 8 8 6 2 2 - ' 1 7 5 4 8 8 INC 3 , 3 0OO23-' 6 2 4 4 1 3 LDA 1, XX •> INCREMENT X 00O24- ' 6 3 6 4 1 4 LDA 2, INCX ; COORDINATE 00O25-" 147663 ADD 2, 1 ; AND OG026 •' 8 4 4 4 1 8 STA 1, XX ; STORE OO027-' 161464 INC 0, 0, SZR DONE L I N E ? 0 0 0 3 6 ' 8 8 0 7 6 3 JMP CONT ; N O - CONTINNUE 8 8 0 3 1 •' 8 6 8 2 4 4 HI 00 44 8 8 0 3 2 - 3 6 6 1 7 7 IN TEH 8 6 8 3 3 - ' 8 8 6 0 8 2 $ J S R 0.FRET ; RETURN GO034 •  0 6 3 7 7 ? 0 O 0 3 5 - 0 0 6 7 7 ? 0OG36-' 6 6 8 0 8 8 6 6 0 3 7 ' 0 0 2 8 3 8 0O640- ' 177777 ; CONSTANTS C 3 7 7 7 : 3 7 7 ? C 7 7 7 : 7 7 7 XX: 6 P P L I N : 2 0 3 8 TNCX: -1 94 60G2 L I HEP END 95 0001 SCAM ; TIIIS SUBROUTING HAS BEEN DESIGNED TO j SCAN A SEQUENCE OF FRAMES i ; REQUIRES HARDWARE IHTEFACE ON DEVICE A O000O1 T I TL. ENT EXTM EXTD NREL TXTH SCAN SCAN GTNUM, FMPOS, CHWRT, WRITE, EDGE, FMSTA, BRGH7 . FCAL 1 > CONSTANTS 00000'• 380227' ARTN: • RTN 0O0O! •  000238' A 008 : A 0:0 8000?'' 8O0231 ' A ACI : ACI 00083' 003232' AA02: - A C 2 88004' 868267' T l : TXT1 -88885- 038384' T2: TXT2 00880 086327' 73; TXT3 88887'- 177777 AGHUH; GTHUM 88818- 866003 D U N : 3 8061 1 -888033. EDGE!: 6 0083 0- 177777 A F S T A t i" MSTA 00013 ABRT: ERGHT 00014' ABRST; BRSTA 00815- 656763 OOriN : STA 3,0ARTN SAVE RETURN 008 10 642763 STA 6,PAAC6 SAVE AC'S 0.0© 1 ? • 646763 STA .1,0 A ALU 80020- 852763 STA 2,0HAC2 80821 182488 SUB 0, O .• SET FLAG TO DETERMINE 80822- 648767 STA O, EDGE1 > EDGE 1 OO023 866771 JSR OABRST SET EDGE1 TO ADJUST INTENSI 0O824 666767 JSR 8 ABRT j ADJUST BRIGHTNESS O0025- 666331* JSR 0.FCAL i CALL EDGE 8002b- EDGE 80027- 068902 08038- 686611' EDGE1 80031• 888618' DUM 00O32- 826757 LDA O,EDGE 1 SET EDGE1 IN FMPOS 00833" 066757 JSR 0AFSTA • 00034• 806768 JSR OABRST ; SET EDGE1 IN BRGHT 88835- 886756 JSR GABRT ; ADJUST BRIGHTNESS 88836'02074b LDA O, T l .: MESSAGE- SCAN'INITIALIZATION 88037- 806567 JSR OAWRT 88843- 620745 SCAN1: LDA O, T2 .; MESSAGE- HOW MANY FRAMES? 08841 -866565 JSR OAWRT 880 42 0O6745 JSR OAGNUM GET RESPONSE 88043 1024OO SUB 8, 0 CHECK FOR 08 04 4 142485 SUB 2, O, SNR BAD RESPONSE 8004 5 0OO773 JMP SCAN1 ; BAD- RE-ENTER 0084S'858565 STA 2,FRAME STORE IT 8804 7- 020737 LDA O, T3 .- MESSAGE- SCAN BEGINS 8805G- O06556 JSR OAWRT 88851 026563 LDA 8, 3 .; ISSUE Z TO GO052 063644 DOC 6, 44 SCANNER 0OO53 960277 STFEN: INTDS 96 0 8 0 2 s c a n 00054' 62856; LDA 8, 060 : WRITE END 8 0v3.; -661122 DO A3 6, MTA ; OF F I L E 008 55' 162466 806 0.- O • FIRST - 1 0G08?-' 101468 •43 8. 6 8084.0 646356 61 A 8, FIRST 0884:1 828556 LDA O,ABUFF : SET UP 00862' 624 536 LDA 1,3BUFF ; . BUFFER 08 865 • 6'i4555 STA i,BUFF : POINTERS 6666.) • 8 36556 LDA 2,. NHL IN : SET UP LINE 80665' 653356 STA 2,YiLIN ; • .COUNTER 08066' 833556 LDA 2, YO ; IN IT IALIZE Y 60667' 656556 STA c: * Y • • COORD I NATE 06676 634556 LDA 3, 020 • BUFFER COUNTER 00071. ' 654556 STA 3 ,BUFLL 60072-' 634564 • LDA 3, 01 ; PRODUCE AUTO INCR. 00073 J 162483 SUB 3. 3 ; ADDRESS 00074' 640621 STA 0, 21 08075-' 163808 • ADD 3, 8 08 :•?>:.-• 872844 ST L.N E: DOB 3, 44 : ISSUE Y COORDINATE 08677' 126488 SUB 1.. 1 i CLEAR ACI. 00106' 836553 LDA 2, 02 : IN IT IALIZE BYTE 001C1' 658558 STA 2, TUO ; COUNTER 001C2-' 636558 LDA 2, X8 :• IN IT IALIZE X 00163-' 358556 STA 2.. X COORDINATE 00164' 634533 LDA 3, FPL y M ; IN IT IALIZE POINT 08165' 854558 STA 3, PL IN ; COUNTER 001 06-' 671144 CONT: DOAS 2, 44 .; ISSUE X COORDINATE 00107' 663644 SKF'DN 4 4 : WAIT FOR 00110' 066777 JMP . - 1 ; SCANNER 881 1 1 •' 876444 DI A 2, 44 ; OBTAIN VALUE 00112' 634545 LDA 3, MASK :• MACK TO 801 13' 173466 AMD 3, 2 j ELEVEN BITS 801 14'151220 MOVZR 2, 2 ; CONVERT TO 08115' 151228 MOVZR 2.. 2 EIGHT 00116' 151228 MOVZR •-• •-> •- c ; BITS 001 17' 147360 ADDS 2, 1 i INSERT AND SWAP 00120' 314531 DSZ TWO ; FIRST OR LAST BYTE? 00121' 666406 JMP . -tt. ; F I R S T - NO STORE 00122' 125386 HOVS 1,1 ; L A S T - SWAP HALVES 00123' 646821 STA 1, 021 J AND STORE 88124' 1264 88 SUB 1 , 1 CLEAR ACI 80125' 638523 LDA 2, C2 ; RESTORE BYTE 88126' 858323 STA 2, TWO : COUNTER 80127- 636524 LDA 2, X :• INCREMENT X 88138' 834538 LDA 3, INCX ; COORDINATE 88131• 173880 ADD 3, 2 • AND 60132- 650521 STA . 2, X i STORE 86133- 814522 DSZ PL IN J LAST POINT IN LINE? 00134- 003752 JMP. CONT i NO- CONTINHUE 88135- 838510 LDA 2, Y ; INCREMENT Y COORDINATE 00136- 634523 LDA 3, INCY 0013? 1730OO ADD 3, 2 86146'8505O5 STA 2, Y 08141• 0145O6 DSZ BUFLL 00142 0004 44 JMP SBr'LL ©0143 024^73 LDA 1,FIRST 80144'125004 MOV 1, 1, SZR • FIRST = 1? 88145- 008413 JMP YES ; YES 88146- 624473 LDA 1, BUFF i NO - SWAP POINTERS 97 0003 S C A N 0 9 ! 47 0 so- 72 01 8 0,BUFF 0 0 \ 70 121000 MOV ; , 0 e o i s i '0 245 1 1 L D A i.WDONT 00 1 5 2 060244 HI 00 44 00] 53 '006510 • ISR 600ART ; CHECK WRITE 00154 '024463 L I : A l . B U F F , SWAP !"'0 INTERS 0 0 1 5 5 '0404 64 OTA 0,BUFF 0 0 1 3 3 ':21003 MOV 1, 0 OOl 5? '003410 JMP R 1 T E 80 1 38 '063622 YES: SKPDN M T A " WAIT-FOR EOF WRITE 09161 ' 0 0 0 7 7 ? J M P . -1 0 0 1 6 2 '126400 SUB ] .. ! F I R S T = 0 8 S 1 3 3 '0444 53 OTA 1,FIRST 0O164 '068222 HI 00 MTA 8 0 1 6 5 063244 • HI 00 4-4 0 8 1 6 6 '660177 IN TEH 8 0 1 6 ? '0682^7 RITE: INTBS 8O1?0 '662622 • DOB O, MTA ISSUE START LOCATION 08171 '624471 LDA 1,WDONT . ISSUE WORD COUNT 0O172 ' 067622 DOC 1, NT A TO MTA 8 8 1 7 3 '324471 LDA 1,C50 ISSUE WRITE 00 1 7 4 '665122 DO AS 5, MTA COMMAND 8 0 1 7 0 • 824444 LDA • 1,BUFF SWAP • 8 0 1 7 5 1 0 4 8 4 4 3 STA OO BUFF . BUFFER 8 0 1 7 ? '121603 MOV 1,8 POINTERS 8 8 2 0 8 '824446 LDA 1,C28 BUFFER COUNTER 0020 1 '6444 4 5 STA 1,BUFLL 8 0 2 8 2 • 634454 LDA 3,0 1 PRODUCE AUTO IIICR. 0 0 2 3 3 ' 152468 SUB 3, O ADDRESS 082O4 ' 840821 S T.A 8, 21 0O285 '163000 ADD 3, 6 0 8 2 8 0 '614435 SBFLL: D32 HLIN LAST L I N E ? 0 0 2 8 7 • 0 0 0 6 6 ? JMP STLHE NO- CONTINUE 0 0 2 1 0 • 068244 HIOC 4 4 YES - CHECK WRITE 0821 1 1 0 2 0 4 3 8 LDA 6,BUFF O0212 '024450 LDA 1,WDCNT 0 0 2 1 3 ' 0 8 6 4 5 8 •JSR 8A0WRT 88214 •814417 DS2 FRAME LAST FRAME? 0 8 2 1 5 ' 338487 JMP' . +7 NO- MOVE TO NEXT 8 8 2 1 6 ' 8 2 8 4 4 7 LDA' 6, T4 DONE MESSAGE 8 8 2 1 7 1 8 0 6 4 6 7 JSR 6 AWRT 8 8 2 2 8 ' 626410 LDA 0, A C 8 RESTORE AC'S 8 8 2 2 1 •824410 LDA 1, ACI 0O222 'O30410 LDA 2, AC2 0 0 2 2 3 '082404 JMP ORTH YES- RETURN 06224 ' 0 0 6 4 4 2 JSR OAFMPS MOVE F I L M 0 0 2 2 5 1 6 6 0 6 2 6 •JMP . STFRM RESTART SCANNING CONSTANTS 8 0 2 2 6 '177777 AWRT: WRITE 8 8 2 2 7 '836030 R T M : 0 8 8 2 3 3 ' 6 8 8 3 0 8 OOO: 6 8 8 2 3 1 '380368 ACI : 6 8 6 2 3 2 '380300 002: 8 8 8 2 3 3 300008 FRAME: 3 8 8 2 3 4 '863777 -; ~f ~? ~f ! 1 1 8 3 2 3 5 '000860 060: 63 8094 SCAM @ 0 2 ? e ' o e e o a o FIRST: C 9 2 i 7 ' 0 0 6 3 3 V ' M B U F F -00240 ' 004357' 3 3OFF: 002 4- 0 0 0 0 0 0 0 :j F F : 0O242-' 000480 NHL IN.-08243-'883888 NLIN: 00244-382480 TO 88245'386686 '.'• 0324 6 •' 885828 828; 0824V'808838 •• 51.: 00253 •' 08O8S2 02: 8825 1 •' 683883 TWO: 33252 - 173466 HO: 88253 ' 600383 X-08254-'006403 PPL IN: 0O253-'808068 PL.IN: 30 2 5 5 - 6 8 6 8 6 1 61: 80257-' 383777 MASK: 00268-'800812 INCH: O0261 •' 177756 i NO'-': 0O262-' 174636 WDCNT: 0326-•]77777 ACWRT-. 00264 •'866838 856: 80265-' 388342-' 74: 0 0 2 6 6 '17777? AFHPS: TNT1: 80267'336412 -8923.3 '.82834.6 00271'028848 00272'651563 00273'646516 88274'828111 00275'8471 1 1 88276'8521 i 1 e8277'6405i4 80300'044532 O0301'04O524 98362'044517 88363'647068 TXT2: 08384'366412 88335'626046 08386'626343 00307'028046 O0310'028043 883!1'64411? 88312'853446 003 13'046381 08314'04713 1 89315'320186 88316'651 16! 88317'646585 00323'651477 88321'828848 00322'623048 08323'023048 3 3 OFF 1 3 OFF? 8 4 8 6 8 24 36 6 28 3 3 -24C8 6 486 6 1 -12 -4868 CHWRT 58 T X T 4 FMPOS :• MESSAGES . TXT ,''<15><12> SCAN INITIALIZATION .TXT ,•-< 1 5 X 1 ? - HOW MANY FRAMES 99 0305 SLAM 0033 • •' 61:0040 o o 32 5 • o 3 o 0 4 0 00326•020006 T:;T~: . TXT /<15 6 0 3 2 ?'0 6 3 4 1 2 00336 •' 626646 60331 ' 620640 00 332'62364 3 03333'3283 43 0O33-' •' 33 i 503 88335 •' 8485 .1 6 08336"828182 30333''34 2537 03340''344516 88341 •'651463 TXT4: .TXT •••''•IS 00342'' 066412 03343'826646 00344-G2O040 88345'' 826648 68346 •' 32364 8 88547-' 651567 0035O-' 64651 6 80351 -' 028164-80352'647516 00353-' 64246? 06354 •' 883467 00355'803467 0O356-' 803466 ..084.080 BURT 1 : . BLK 4680 064800 B U F F 2: . BLK 4800 . END SCttM BEGINS." SCAN D0NE<7><?><?><?><? 100 8001 SCAR!! ; MAIN PROGRAM FOR SCANNING FILM ; i CALLED FROM A FORTRAN PROGRAM BY ; C A L L SCANN ; 008081 T I TL ENT E X T N EXTD NREL TXTM SCANNER SCAN N TAPOS, CSKIP, FIFOS, SCAN .CPYL,.FRET 000068 FS. =0 O000O-'906688 FS. 80001 •  636881SSCANN: JSR 0.CPYL ; 88882'628422 BEGIN: LDA " 8,QUES1 ; 0O083'0244 14 LDA 1, ATAPE 80O84'006414 JSR OASKIP 00065'020443 LDA 0, QUES2 00866'0244 13 LDA 1,A FIL M 0008?'6064U JSR OASKIP 00618'8284 04 LDA 8,QUES3 88611'624411 LDA 1,ASCAN 00O12'O0S4O6 JSR OASKIP 00013'028585 LDA O, QUES4 OO014'624467 LDA 1,ASTPT 88615'866483 JSR OASKIP 00016'806082* JSR 6.FRET ; RETURN ENTRY. TAPE POSITIONING? ; FILM POSITIONING' ! SCANNING? MORE TO DO OO017'177777 ATAPE: TAPOS 0OO20'177777 ASKIP: CSKIP OO021'177777 AFILM: FIPOS 88822'177777 ASCAH: SCAN 0OO23'0080O2'ASTRT: BEGIN ; CONSTANTS 88624'886825'QUES1 : + 1 TXT • <15X12> IS TAPE POSITIONING DESI 88825 80026 88827 88830 88831 88032 88833 08O34 88835 86036 0003? O0846 80O41 0O842 0OG43 08844 O0045 '066412 'O201 11 '051448 'O52101 '858165 '628126 '847523 '644524 '844517 '847111 '847107 '628164 '642523 '644522 '642584 '837443 '620648 101 QB02 SCANN 0 9 0 4 6 •' 0 2 : 0 0 4 0 £ 1 0 0 4 7 ' 0 2 0 0 0 0 0 0 0 5 0 •• 0 0 0 0 5 1 •' 3UES20 . +1 . TNT ' GO055 ' S0S412 0 O G 5 2 ' 0 2 0 1 1 i O0O53'051440 0O854'043111 8S855'04O115 8 8 8 5 6 ' 0 2 0 1 2 0 80857'047523 6 0 8 6 3 ' 0 4 4 5 2 4 06861'844517 06362'347111 86863'847,8? 66864'828104 06065'042523 66066'644522 6066?'042534 66070'637440 66671'02OG4O 06372'020048 0O873'820000 O0O74'888675'QUES3: .+1 . TNT 88075'686412 88876'828111 06877'851440 88188'851503 88181'040516 .031.62' 0471 1 1 081O3'0471O7 00164'628104 001O5'042523 001O6'044322 00107'642504 0 0 U 8 ' 83744O 801 11'620040 88112'626048 86113'026640 881 14'820O40 881 15'826040 881 16'026040 881 17'826080 88120' 806121 ' P.UES4: .+1 . TXT 88121'806412 68122'020184 08123'847440 00124'054517 08125'652440 88126'653511 00127'651513 68136'828124 88131'847440 88132'641517 86133'047124 O0134'044516 80135'652585 ,-'<15><l?> IS FILM POSITIONING DESIRED? /<15><12> IS SCANNING DESIRED? • <15X12> DO YOU WISH TO CONTINUE? 0 0 G 3 SCANN 6013S-837440 00137'028848 0O140'02004Q 0 0 1 4 1' O 2 3:'! 4 0 80 142'0 2S848 • 0O143'028888 103 88G1 TAP;OS ; THIS SUBROUTINE HAS BEEN DESIGNED TO ; POSITION THE MAGNETIC TAPE j IF USED IN CONJUNCTION WITH DOS.. THE i FILE DIRECTORY DATA WILL NOT CORRESPOND ; WITH THE PRESENT TAPE POSITION . T I T L TAPOS . ENT TAPOS . EXTN GT.HUH, WRITE, C S K I P . HREL OOOOOl . TNTH 1 0 0 0 0 0 ' 0 5 4 4 6 5 TAPOS: • STA 3, RTN ' ; SAVE RETURN 8 0 0 0 1 ' S 4 0 4 6 5 STA 0, A CO :• SAVE AC'S 8 0 0 0 2 ' 6 4 4 4 6 5 STA i , ACI 8 8 0 0 3 ' 8 5 0 4 6 5 STA 2, A C 2 80O84'026477 LDA O,TXT1 M E S S A G E - T A P E P 0 S IT I 0 HI N G 0O6 6 5 ' 8 0 6 4 7 3 JSR OAWRT 80 6 8 6 ' & 2 6 5 1 2 MORE: LDA 0,TXT2 ; MESSAGE- ENTER COMMAND 66607'O06471 JSR OAWRT OO016'826538 LDA O, TNT3 .; MESSAGE- (.COMMAND CHOICES) 80 0 1 i •  886467 JSR OAWRT 80 G 1 2 ' 6 6 6 4 6 5 JSR ©AG NUM GET COMMAND 8 0 0 1 3 ' 1 0 2 4 6 8 SUB 0, 0 CHECK FOR BAD 00O 1 4 ' 1 4 2 4 8 5 SUB 2, O, SNR ; RESPONSE 80O15'808771 JMP MORE ; BAD- RE-ENTER 6 8 0 1 6 ' 0 2 0 4 5 5 LDA O, C4 ; CHECK FOR BAD OO017'142512 SUBL# 2, 0, SZC ; RESPONSE .00620 '.08? 766 JMP MORE ; BAD- RE-ENTER 0 0 S 2 1 ' 0 5 0 4 5 8 STA 2,CMND j STORE IT O0O22'S2045O LDA 0, C2 • ; I S I T A 80 O 2 3 ' 1 1 2 4 1 5 SUBS 0, 2, SNR TWO? 8 8 0 2 4 ' 8 6 6 4 3 5 JMP RETRN Y E S - RETURN 8 8 8 2 5 ' 6 2 8 4 4 7 LDA 0, C l .1 NO- I S IT A 8 8 8 2 6 ' 6 4 8 4 4 7 STA 8,TIMES ; ONE? (ALSO 8 6 8 2 7 ' 1 1 2 4 1 5 SUBS 0,,2, SNR ; SET TIMES=1> 8 8 6 3 6 ' 0 0 8 4 1 8 JMP MOVE ; YES - DO MOVE 8 0 0 3 1 ' 6 2 6 5 3 7 TAP01: LDA 8, TXT4 ; NO- MESSAGE- HOW MANY TIME 00O32'086446 JSR OAWRT 8 8 8 3 3 ' 8 6 6 4 4 4 JSR 0AGNUM ; GET NUMBER 8 6 6 3 4 ' 1 6 2 4 8 6 SUB 8, O i CHECK' FOR 8 8 8 3 5 ' 1 4 2 4 8 5 SUB 2, 8, SNR ; BAD RESPONSE 8 6 8 3 6 ' 8 8 6 7 7 3 JMP TAP01 ; BAH- RE-ENTER 0 8 8 3 7 ' 0 5 8 4 3 6 STA 2,TIMES ; STORE IT 8 0 0 4 6 ' 0 2 8 4 3 1 MOVE: LDtt O, CMND ; SHIF T COMMAND 80O41 •' 1O3120 ADDZL O, O ; LEFT THREE 8 8 8 4 2 ' 1 0 1 1 2 6 HOVZL O, G ; B I T S 80O43'126488 SUB 1, 1 ; ZERO ACI 0OO44'866277 INTDS 6 8 0 4 5 ' 8 6 7 8 2 2 LOOP: DOC 3,MTA ; I S S U E ZERO WORD COUNT 0 8 6 4 6 ' 8 6 1 1 2 2 DO AS 0,MTA i I S S U E COMMAND AND START 0OO47'876422 D I A 2,MTA ; CHECK I F TAPE 8 8 6 5 8 ' 1 5 1 2 S 3 MOVR 2, 2, SNC * READY 8 0 6 5 1 ' 8 6 8 7 7 6 JMP NO- WAIT 8 6 0 5 £ ' 0 1 4 4 2 3 DSZ TIMES ; YES - DO IT A G A I N ? 8 0 8 5 3 ' 6 8 0 7 7 2 JMP LOOP ; YES- GO BACK AGAIN 8 0 0 5 4 ' 0 6 0 2 2 2 NIOC MTA :• NO- CLEAR FLAGS 88855'06O177 INTEN 104 0 0 8 2 T A P O S 8 8 0 5 S ' 0 2 8 5 4 2 L D A 6 0 6 5 7 ' 6 2 4 4 2 3 L E A 0 O O G 8 ' 0 0 5 4 2 1 J S R 0 0 0 5 1 ' 8 2 0 4 0 5 R E T R N - L D A 0 O O 6 2 ' 0 2 4 4 0 5 L D A 0 0 8 6 5 ' 0 5 0 4 8 5 L D A O 0 O 6 4 ' 8 8 2 4 8 1 J M P 0. , T X T 5 .: M E S S A G E - A M Y M O R E ? 1, A M O R E O A S K I P 0, A C O ; R E S T O R E A C S 1.. A C I 2, AC2 6 R T N Y E S - R E T U R N ; . C O N S T A N T S 0 O 0 6 5 ' 8 0 8 6 6 ' 0 0 0 6 ? ' 0 8 8 7 0 ' O 8 0 7 1 ' 8 8 8 7 2 ' 0 0 0 7 3 ' 8 O 0 7 4 ' 0 O 0 7 5 ' 0 0 0 7 6 ' 0 0 0 7 7 ' G 0 1 8 8 ' 8 8 1 0 1 ' 8 8 1 8 2 ' 808008 088080 083868 633838 833383 388082 863334 88838! 868883 6681 16 17 7 7 7 7 1 I I I ! I 177777 177777 886006-RTN: ACS: A01 : A3? : SMNB: 32: 04: 01 : TIMES: CN: AGNUM: HURT: ASKIP: AMORE: O 6 0 8 6 4 1 0 1 1 6 • G T N U M W R I T E C S K I P M O R E 8 0 1 0 3 ' 8 8 0 1 0 4 ' T K T 1 8 8 1 6 4 ' 8 0 1 6 5 ' 8 8 1 6 6 ' 0 0 1 0 7 ' 8 8 1 1 0 -0 0 1 1 ! • 8 8 1 1 2 -8 8 1 1 3 -8 8 1 1 4 -8 8 1 15-6 6 6 4 1 2 6 2 8 8 4 3 6 2 6 6 4 6 8 5 2 1 6 1 6 5 6 1 6 5 8 2 3 1 2 8 6 4 7 5 2 3 0 4 4 5 2 4 0 4 4 5 1 7 8 4 7 1 1 1 8 8 1 1 6 ' 6 4 7 1 6 7 G 0 1 1 7 ' 8 0 0 8 8 0 8 0 1 2 0 ' 0 0 0 1 2 1 ' T X T ; 0 0 1 2 1 ' 8 8 1 2 2 ' 8 8 1 2 3 ' 8 8 1 2 4 ' 8 8 1 2 5 ' 8 8 1 2 6 ' 8 0 1 2 7 ' 8 8 1 38-' 0 0 1 3 1 ' 8 8 1 3 2 ' 8 8 1 3 3 ' 8 8 1 3 4 ' 0 0 1 3 5 ' 8 6 1 3 6 ' 8 0 1 3 7 ' 8 8 1 4 0 ' 6 6 6 4 1 2 0 2 O 0 4 0 0 2 0 O 4 O 6 2 6 8 4 0 8 2 6 8 4 8 0 4 2 5 1 6 6 5 2 1 6 5 0 5 1 O 4 0 6 5 2 1 0 1 0 5 8 1 0 5 0 2 8 1 0 3 6 4 7 5 1 5 0 4 6 5 O 1 6 4 7 1 8 4 © 3 5 0 O 0 0 O O 1 4 1 ' TXT: + 1 TXT + 1 T X T . + 1 . T X T ; M E S S A G E S /< 1 5 X 1 2 > T A P E P O S I T I O N I MG.-' • < 1 5 > < 1 2 > E N T E R T A P E C O M M A N D : / / - < 1 5 > < 1 2 > K R U D ) , 2 < H 0 N E > , 3 < F S F > , 8 0 1 4 1 ' O 0 6 4 1 2 o © Gi G) © G' G> G> IS Gi <s> Gi Gi Gi •3 Gi IS Gi ro ro ro r-o ro ro ro ro ro ro '..j OJ OJ ro r-o r-o ro ro ro ro ro Gi -j cr, cn OJ ro v— -. -. •. •-G> Gi CJ Gi Gi CD CD Gi IS IS j . Ji. Cil cn Cl ro ro ro ro cs -J ro i-. G' CD '3 Gi 3 Cr, on cn »-* cn K* Gi Q Gi Gi i-. t-. J i J i y-. a-. Cl »— -J CD '3 Gi o ro IS IS IS IS IS IS IS IS IS IS IS IS G> Q CD IS Q CD Q CD Gl Gi G> G> Gi >S Gi Gi Gi Gi Gi Gi >S CD >S <S CD G' G' G' Gi CD Q CD CD Gi Gi CD ro ro ro ro ro ro ro ro ro ro ro ro ro ro ro ro ro i-. i -PO i-* i-* i- 4 »-* ^ I - * G' G' CD G< CD Q G» '2D -J --J -J --J --J --J -\! o -j 0". cn * OJ ro © --J ci cn jv. oi ro >- © -j c\ cn * w r-o V •. •-O Gi Gi G' CD G» G' Gi CD 3 G' Gi '3 CD CO CD '3 G> G' Cv' G' G> G' CD Gi ro ro ro ro ro ro r-o ro ro ro OJ ro cn J. ro ro ro ro GJ CO '3 Gi Gi Gi GJ <l'! Co Gi G> G' -J ro A G' "-J Cr, OJ * Gi Gi © © Ch rO Gi CD Gi Gi Gi CD IS CD '3 Gi Ji 01 Ul >-- CH Ji CO IS CD CO -P-ro GI j i j . j-. -Si j . - f . j i j i -r. ro ^  ro OJ o .r. j * j * j i *-> I— Gi Gi G' G' '3 G> is 'Gi '3 Gi Gi C.J CH J" •— '3 --J CD CD Gi C) ro G> CD G' 'Gi Gi Gi G' G' G' Gi G' CD G* ' 3 G' G* G' CD Gi ' 3 ' 3 G> G) Gi 'Gi G' G' G' G' G' G' G' G' G' GT G' G' G! Gi Q G' CD G' 'G CD G' --J ci Ci cr, 'J-. cr, oi Gi cr, cr cn cn a -J cr, cn j i OJ ro >•- G --J oi cn cn cn cn cn CJ ro I-* a r.-. j-. j . j . ji j-. •j cn cn .r. o; ro G' CD G' CD CO ' 3 G' CD G' ' 3 ' 3 G' G' CD ' 3 G' CD G- G.' G' CD G' G' G> ro ro ro cn ro ro ro cn r-o ro ro J. -ii OJ ro A ro r-o ro ro G CD G' Ji. ^ J i CD J i '-' J . G ! -f- -J -•' i--> Ol ro I-- 1 CD CD ' 3 '3 Cj '-- CD G' J i Cf! '-• CD IS. (- CD i i H H CD CD © i - J . CD CD O ' 3 -j co J. J. G' ro or-, ci CD CD o-i cn <:;• cn .(•- cn ro cn -5. J-. J» >- CD is G> o-i ro -r- OD cr, OJ J* cn <s •- -J -3 G' 3 3 3 ;-c -H cn H + X i-> H + DC I-* — I \ cn v A . cn O CO 0 3 : X--c 2: m o 2: m I'-2: -c c1 •71 0004 TAPOS 68233'643446 8 8 2 3 4 ' 6 4 2 i 1 7 66243'6544 40 66241'640517 68242'051135 O O 2 4 3' 6 3 7 4 4 0 00244'020348 08245'020040 6824b'820048 0024?'823000 107 0001 WRITE ; THIS SUBROUTINE HAS BEEH DESIGNED TO ; OUTPUT (\ LINE OF TEXT TO THE TELETYPE ; USING THE PCHftR INSTRUCTION i ACO MUST CONTAIN THE LOCATION OF j THE LINE TO BE WRITTEN ; THE LINE MUST BE WRITTEN IN TXTM 1 ; -FORMAT .TITLE WRITE .EHT WRITE . NREL 0000O'054430 WRITE: STA 3, RTN SAVE RETURN O0OO1 040438 STA ; SAVE AC'S OOOOO•' 044430 STA 1, ACI O0863'656438 STA 2, AC2 66604'646431 STA O, DDR ; STORE ADDRESS 86665'622438 LOOP: LDA O, ODDR OBTAIN TWO CHARACTERS 80603'610427 ISZ DDR INCREMENT ADDRESS 86667'824425 LDA 1, 0177 MASK 86818'167400 AND 6, 1 ; SECOND IN ACI 68811'122765 SUBS 1, 0, SNR .: FIRST IN AC6-2ER0? 0O012'O0O412 JMP END .; YES 66813'886662 .SYSTM ; NO-OUTPUT FIRST 68614'876006 .PCHAR 60015'880400 JMP 66816'121085 MOV 1,0, SNR ; SECOND ZERO? •0O0i7-O3-O4o5 JMP -END YES 0OG20'086602 .SYSTM ; HO-OUTPUT SECOND 08021'676606 .PCHAR 68022'036406 JMP 00823'868762 JMP LOOP ; NEXT TWO 86624'826405 END: LDA O, ACO RESTORE AC'S 68625'824465 LDA 1, ACI 88826'030465 LDA 2/AC2 88627'862461 JMP ORTN RETURN ; CONSTANTS 8003O'OOGOOO RTN: 0 0O031'000830 A 68 : O 8O032'066803 ACI : O 88833'668368 A 0:2: 8 60034'866177 0177: 177 88835'668888 DDR: 6 . END 108 REFERENCES 1. E.R. Kretzmer, " S t a t i s t i c s of t e l e v i s i o n s i g n a l s " , B e l l Syst. Tech. J . , V o l . 31, July 1952, pp. 751-763. . 2. W.F. Schreiber, "The Measurement of t h i r d order p r o b a b i l i t y d i s t r i -butions of t e l e v i s i o n s i g n a l s " , IRE Trans. Inform. Theory, V o l . IT-2, Sept. 1956, pp. 94-105. 3. J.O. Limb, "Entropy of quantized t e l e v i s i o n s i g n a l s " , Proc. Inst. E l e c . Eng. (London), V o l . 115, Jan. 1968, pp. 16-20. 4. P.R. Wallace, "Real-time measurement of element differences i n t e l e v i s i o n programs", Proc. IEEE ( L e t t ) , V o l . 54, Nov. 1966, pp. 1576-1577. 5. A.J. Seyler, "Real-time recording of t e l e v i s i o n frame di f f e r e n c e ar-eas", Proc. IEEE ( L e t t . ) , V o l . 51, March 1963, pp. 478-480. 6. A.J. Seyler, " S t a t i s t i c s of t e l e v i s i o n frame d i f f e r e n c e s " , Proc. IEEE ( L e t t . ) , V o l . 53, Dec. 1965, pp. 2127-2128. 7. L.E. Franks, "A model f o r the random video process", B e l l Syst. Tech. J . , Apr. 1966, pp. 609-630. "8. -W.-K. Pr a t t , J . -Kane, and H.C. -Andrews, "Hadomard transform image cod-i n g " , Prroc. IEEE, V o l . 57, Jan. 1969, pp. 58-68. 9. A. Habibi and P.A. Wintz, "Image coding by l i n e a r transformation and block quantization", IEEE Trans. Commun. Technol., V o l . COM-19, Feb. 1971, pp. 50-62. 10. G.B. Anderson and T.S. Huang, "Piecewise f o u r i e r transformation f o r pi c u t r e bandwidth compression", IEEE Trans. Commun. Technol., V o l . COM-19, Apr. 1971, pp. 133-140. 11. M. Tasto and P.A. Wintz, "Image coding by adaptive block quantization", IEEE Trans. Commun. Technol., V o l . COM-19, Dec. 1971, pp. 957-971. 12. A. Habibi, "Comparison of nth-order DPCM encoder with l i n e a r trans-formations and block quantization techniques", IEEE Trans. Commun. Technol., V o l . COM-19, Dec. 1971, pp. 948-956. 13. J.B. O'Neal, J r . , " P r e d i c t i v e quantization systems ( d i f f e r e n t i a l pulse code modulation) f o r the transmission of t e l e v i s i o n s i g n a l s " , B e l l  Syst. Tech. J . , V o l . 45, May/June 1966, pp. 689-721. 14. J.B. O'Neal, J r . , "Abound on si g n a l - t o - q u a n t i z a t i o n noise r a t i o s f o r d i g i t a l encoding systems", Proc. IEEE, V o l . 55, March 1967, pp. 287-292. 1 15. K.Y. Chang, "Analysis and optimization of d i f f e r e n t i a l PCM systems operating on noisy communication channels", Ph.D. t h e s i s , Oct. 1972, Dept. E l e c . Eng., U.B.C. 109 16. R.C. Brainard, F.W. Mounts, B. Prasada, "Low r e s o l u t i o n T.V.: sub-j e c t i v e e f f e c t s of frame r e p e t i t i o n and p i c t u r e replenishment", B e l l Syst. Tech. J . , V o l . 46, Jan. 1967, pp. 261-271. 17. F.W. Mounts, "Frame-to-frame d i g i t a l processing of TV pict u r e s to remove redundancy", Symp. Picture Bandwidth Compression, MIT, Cam-bridge, A p r i l 1969, pp. 653-672. 18. J.E. Cunningham, "Frame-correction coding", Symp. Pi c t u r e Bandwidth  Compression, MIT, Cambridge, A p r i l 1969, pp. 623-652. 19. L.C. Wilkins, P.A. Wintz, "Bibliography on data compression, p i c t u r e properties and pi c t u r e coding", IEEE Trans. Inform. Theory, V o l . IT-17, March 1971, pp. 180-197. 20. H.M.R. Souto, The Technique of the Motion Pi c t u r e Camera, Focal. Press Ltd., 1969. 21. N.G. Deryugin, "The power spectrum and au t o c o r r e l a t i o n of the t e l e -v i s i o n s i g n a l " , Telecommunications, V o l . 7, 1957, pp. 1-12. 

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