Visual attention based image quality assessment

Inspired by the success of structural similarity index (SSIM), some image quality assessment (IQA) methods have been developed recently. To achieve better performance, this paper proposes a new visual attention (VA) model that combines saliency based VA and visual importance based VA, under the assumptions that humans often pay more attention to the regions with important content in the beginning of evaluating a given image and then the regions with poor quality. Then the proposed VA model is incorporated into SSIM. The experiments on LIVE database and TID2008 database demonstrate its improvements over the latest state-of-the-art IQA methods and the information content weighted SSIM measure (IW-SSIM).