UBC Theses and Dissertations
Rate allocation for Markov model aided convolutional coding of vector quantized image data Lin, Xiaoping
This thesis proposes an optimally rate allocated image transmission system that uses Vector Quantization (VQ) for source coding, and a family of variable rate Punctured Convolutional Codes (PCCs) for channel coding. At the receiver, we apply the source aided channel decoding technique known as Markov Model Aided Decoding (MMAD). Our optimality criterion is to maximize the average end-to-end Reconstruction Signal-to-Noise Ratio (RSNR) under the constraints of a fixed information rate (in pixels per second) and a fixed transmission bandwidth. For a given channel SNR, this joint source-channel coder design achieves the optimal rate allocation between the source coding and the channel coding operations. Compared with the conventional rate allocated system (the analogous system that does not use MMAD), the proposed system gives significant performance improvement. This is due to the fact that MMAD increases the strength of the channel codes, thus allowing the system to allocate more rate to the source coder, which results in a higher resolution image. In the course of our study, we first investigate MMAD without explicit channel coding for VQ image transmission over the noisy memoryless channels comprising the Binary Symmetric Channel (BSC) and the Additive White Gaussian Noise (AWGN) channel. In order to evaluate the effects of the order of the Markov model of the data, we consider two types of decoding algorithms. One is based on the Viterbi sequence decoding algorithm, the other is based on the Bahl, Cocke, Jelinek and Raviv (BCJR) decoding algorithm. The former is computationally less complex, and is optimal (in the sense of minimizing the Bit Error Rate (BER)) for decoding with a first order (O(l)) model; while the latter allows an efficient, but slightly sub-optimal, decoding algorithm for decoding with a second order (0(2)) model. We find that most of the MMAD coding gain is already achieved by using the 0(1) model, and therefore in the remainder of the study consider it only. We go on to analyze two types of O(l) MMAD with Convolutional Codes (CCs) employed for explicit channel coding. We call the decoders Markov Model Aided Convolutional Decoders (MMACDs), and show via simulation that the performance benefits attained by using the Markov model are similar to the large gains found for MMAD without explicit channel coding. One type of MMACD is based on the Viterbi algorithm, and applies a trellis merging technique. This decoder has an optimal BER performance, but has the constraint that the length of the source codewords be less than the memory of the CC. The other MMACD is a concatenation of a soft-output channel decoder followed by an MMAD without channel coding. This decoder does not have the constraint on the length of the source codewords, but has less coding gain than the trellis merged decoder. Finally, we investigate the problem of optimal rate allocation between the source coding and the channel coding for VQ/PCC transmission systems that employ MMAD. Our simulation results over the AWGN channel show that the optimal rate allocated system is superior in RSNR performance to the optimally rate allocated system without MMAD. The MMAD coding gain depends on the image, but is typically 2 dB in channel SNR. We find that for the conventional system, the point of optimal rate allocation is fairly independent of the image; while for the MMAD system the allocation depends strongly on the image characteristics. Because of this, the rate allocation calculation is significantly more complicated when using MMAD. The rate allocated systems require an estimate of the channel SNR. Because in practice there will always be some inaccuracy in estimating this, to conclude our study we investigate the sensitivity of the rate allocated systems to channel mismatch, and find them to be fairly robust.
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