Blind image and video quality assessment using natural scene and motion models
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We tackle the problems of no-reference/blind image and video quality evaluation. The approach we take is that of modeling the statistical characteristics of natural images and videos, and utilizing deviations from those natural statistics as indicators of perceived quality. We propose a probabilistic model of natural scenes and a probabilistic model of natural videos to drive our image and video quality assessment (I/VQA) algorithms respectively. The VQA problem is considerably different from the IQA problem since it imposes a number of challenges on top of the challenges faced in the IQA problem; namely the challenges arising from the temporal dimension in video that plays an important role in influencing human perception of quality. We compare our IQA approach to the state of the art in blind, reduced reference and full-reference methods, and we show that it is top performing. We compare our VQA approach to the state of the art in reduced and full-reference methods (no blind VQA methods that perform reliably well exist), and show that our algorithm performs as well as the top performing full and reduced reference algorithms in predicting human judgments of quality.