Statistical Video Models and Their Application to Quality Assessment

Quality assessment plays a very important role in almost all aspects of multimedia signal processing such as acquisition, coding, display, processing etc. Several objective quality metrics have been proposed for images, but video quality assessment has received relatively little attention. Most of the video quality metrics in the literature are simple extensions of metrics for images. In this paper, we integrate natural image statistics and the theory of optical flow to propose a new model for the statistics of video signals in the wavelet domain. This model utilizes motion information in video sequences, which is the main difference in moving from images to video. Results are presented to demonstrate the effectiveness of this model to describe the statistics of wavelet coefficients. We then briefly describe how this model can be used in an information theoretic framework to develop quality metrics for video sequences.