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A visual attention model for high dynamic range (HDR) video content

University of British Columbia

High dynamic range (HDR) imaging is gaining widespread acceptance in computer graphics, photography and multimedia industry. Representing scenes with values corresponding to real-world light levels, HDR images and videos provide superior picture quality and more life-like visual experience than traditional 8-bit Low Dynamic Range (LDR) content. In this thesis, we present a few attempts to assess and improve the quality of HDR using subjective and objective approaches. We first conducted in-depth studies regarding HDR compression and HDR quality metrics. We show that High Efficiency Video Coding (HEVC) outperforms the previous version of compression standard on HDR content and could be used as a platform for HDR compression if provided with some necessary extensions. We also find that, compared to other quality metrics, the Visual Information Fidelity (VIF) quality metric has the highest correlation with subjective opinions on HDR videos. These findings contributed to the development of methods that optimize existing video compression standards for HDR applications. Next, the viewing experience of HDR content is evaluated both subjectively and objectively. The study shows a clear subjective preference for HDR content when individuals are given a choice between HDR and LDR displays. Eye tracking data were collected from individuals viewing HDR content in a free-viewing task. These eye tracking data collected are utilized in the development of a visual attention model for HDR content. Last but not least, we propose a computational approach to predict visual attention for HDR video content, the only one of its kind as all existing visual attention models are designed for HDR images. This proposed approach simulates the characteristics of the Human Visual System (HVS) and makes predictions by combining the spatial and temporal visual features. The analysis using eye tracking data affirms the effectiveness of the proposed model. Comparisons employing three well known quantitative metrics show that the proposed model substantially improves predictions of visual attention of HDR.

Text

2015-01-06

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/51777

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/