Motion is one of the most important types of information contained in natural video, but direct use of motion information in the design of video quality assessment algorithms has not been deeply investigated. Here we propose to incorporate a recent model of human visual speed perception [Stocker & Simoncelli, Nature Neuroscience 9, 578-585 (2006)] and model visual perception in an information communication framework. This allows us to estimate both the motion information content and the perceptual uncertainty in video signals. Improved video quality assessment algorithms are obtained by incorporating the model as spatiotemporal weighting factors, where the weight increases with the information content and decreases with the perceptual uncertainty. Consistent improvement over existing video quality assessment algorithms is observed in our validation with the video quality experts group Phase I test data set.
A human visual speed perception model was developed REF to estimate the motion information, the perceptual uncertainty, and an associated spatiotemporal weight for quality assessment of videos.
Video Quality Assessment Using a Statistical Model of Human Visual Speed Perception
ICIP
Physics Medicine