Efficient video quality assessment based on spacetime texture representation

Most existing video quality metrics measure temporal distortions based on optical-flow estimation, which typically has limited descriptive power of visual dynamics and low efficiency. This paper presents a unified and efficient framework to measure temporal distortions based on a spacetime texture representation of motion. We first propose an effective motion-tuning scheme to capture temporal distortions along motion trajectories by exploiting the distributive characteristic of the spacetime texture. Then we reuse the motion descriptors to build a self-information based spatiotemporal saliency model to guide the spatial pooling. At last, a comprehensive quality metric is developed by combining the temporal distortion measure with spatial distortion measure. Our method demonstrates high efficiency and excellent correlation with the human perception of video quality.