CIF:RI:Small:Content-Based Strategies of Image and Video Quality Assessment

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AbstractEmerging demands on ubiquitous multimedia access continue to push coding algorithms to ca-pitalize on content-based properties of images and video. For example, by identifying and pre-serving regions-of-interest, or by synthesizing textures at the decoder, it is possible to dramati-cally reduce bandwidth requirements while preserving visual quality. These next-generation coding strategies must be accompanied by next-generation quality assessment algorithms that can handle the unique coding artifacts. Yet, determining quality in a manner that agrees with human perception remains a grand research challenge. Current quality assessment methods use a fixed analysis, whereas human perception adapts to the image?s content. In order to meet increasing demands on bandwidth, mobility, and IP streaming, there is a critical need to push the state-of-the-art in quality assessment toward such a content-adaptive approach.In this research, the investigator conducts a series of studies designed to examine the utility of content-adaptive models of human vision for quality assessment of images/video containing degradation and enhancement. The first study will collect a large set of subjective ratings for enhanced and degraded images and video. This effort will provide ground-truth data for training and validation. Using these data, the investigator will: (1) Research new methods of quality as-sessment that can deal with images containing enhancement. (2) Research and model the mul-tiple strategies employed by the human visual system during quality assessment, including de-veloping content-based neural models and image-adaptive techniques of strategy selection. (3) Research the relationship between quality and regions-of-interest. This research will lead to more accurate and robust methods of quality assessment, and it will lay the groundwork for next-generation perceptual models that take into account the adaptive nature of human vision.