A full reference video quality measure based on motion differences and saliency maps evaluation

While subjective assessment is recognized as the most reliable means of quantifying video quality, objective assessment has proven to be a desirable alternative. Existing video quality indices achieve reasonable prediction of human quality scores, and are able to well predict quality degradation due to spatial distortions but not so well those due to temporal distortions. In this paper, we propose a perception-based quality index in which the novelty is the direct use of motion information to extract temporal distortions and to model the human visual attention. Temporal distortions are computed from optical flow and common vector metrics. Results of psychovisual experiments are used for modeling the human visual attention. Results show that the proposed index is competitive with current quality indices presented in the state of art. Additionally, the proposed index is much faster than other indices also including a temporal distortion measure.