Saliency and texture information based full-reference quality metrics for video QoE assessment

In this paper, we discuss how to assess video Quality of Experience (QoE) with image saliency and texture information extracted from original and distorted video sequences. Based on this information, we proposed two categories of full-reference quality metrics. The first category of metrics considers spatial distortions measured by MSE and SSIM in terms of saliency weighted images, saliency maps and texture maps. The second category of metrics are constructed by considering temporal distortions, which also includes the above three aspects. Then the temporal MSE between original and distorted video sequences is computed frame-by-frame. Thus altogether 9 metrics are obtained. With these metrics and subjective MOS from both the LIVE dataset and our own dataset, we conduct Neural Net fitting to measure the performance. Finally the detailed comparisons with mainstream models verify the effectiveness of the proposed model.