Video quality assessment based on content-partitioned approach and motion compensation
基于内容划分和运动补偿的视频质量评价

Current structural similarity based image quality assessment algorithm is generally the overall image quality analysis. However, for human visual system, different regions in image have different visual sensitivities, and the current method can not reflect these differences effectively. In this view, this paper proposed a content-partitioned image quality assessment algorithm, which partitioned an image into four regions according to their different gradient magnitudes and assessed their qualities respectively. Then, based on motion compensation, this paper proposed a frame-weighting approach, which extended the proposed algorithm to video quality assessment. The experiments show that the proposed algorithm is more accurate than several current popular algorithms.