Self-Information Weighting for Image Quality Assessment
Several recently proposed image quality assessment (IQA) methods involve a two-stage structure: local distortion measurement followed by pooling. Based on the hypothesis that more weights should be assigned to the image components that contain more information, this paper explored the potential of a Shannon Self-Information based pooling strategy, where self-information measures the surprisal of seeing a local image patch in the context of its surround. We combined the self-information based pooling strategy with the multi-scale structural similarity (MS-SSIM) index, yielding a selfinformation weighted SSIM (SI-SSIM) approach. Extensive evaluations based on six publicly available databases show that the proposed SI-SSIM approach achieves superior or comparable performance as compared with a number of competitive IQA algorithms.
Peng Peng Ze-Nian Li
School of Computing Science Simon Fraser University Burnaby, BC, Canada
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
1758-1762
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）