No-Reference Video Quality Assessment Based on Visual Memory Modeling

Objective video quality assessment plays an important role in a variety of video processing applications such as video compression, transmission, visualization, and display. This paper proposes a no-reference video quality assessment model based on visual memory understanding. Inspired by the findings of neuroscience researchers, who argue there is a large overlap between the active human brain area when performing video quality assessment and saliency detection tasks, saliency maps are employed here to assist the quality assessment. To this end, we first generate the saliency maps using CLBP (Complete Local Binary Patterns) features of the residual frames. Then, a model of visual memory is created from the statistics of saliency maps. This is followed by learning the video quality from the visual memory, saliency, and frame features through a support vector regression pipeline. The experimental results on the state-of-the-art LIVE and SJTU video datasets indicate that the proposed no-reference video quality assessment algorithm is effective and performs statistically better than several other state-of-the-art approaches.