Video Quality Model for Space-Time Resolution Adaptation

Abstract

Delivering voluminous amounts of video data through limited bandwidth channels is a challenge affecting billions of viewers. Accordingly, it is becoming more important to understand the perceptual effects that arise from various dimension reduction methodologies. Towards this direction, we propose a new video quality model that predicts the perceptual quality of videos undergoing varying levels of spatio-temporal subsampling and compression. The new model is established upon the natural statistics principle of videos, which leverage the fact that pristine videos obey statistical regularities that are disturbed by distortions. We found that there exist space-time paths between video frames that best preserve the statistical regularity inherent in the spatial structure of the video frames. The distribution features extracted from frame differences displaced in the direction of these paths correlate more highly with human subjective quality opinions than those from non-displaced frame differences. Given that non-displaced frame differences are widely utilized in video quality models, the improved efficiency of spatially and/or temporally displaced (possibly by more than one frame) frame differences, is an important finding that may significantly elevate the success of studies on temporal features and video quality.