Visual-saliency-enhanced image quality assessment indices
Modern image quality assessment (IQA) indices, e.g. SSIM and FSIM, are proved to be effective for some image distortion types. However, they do not exploit the characteristics of the human visual system (HVS) explicitly. In this work, we investigate a method to incorporate the human visual saliency (VS) model in these full-reference indices, and call the resulting indices SSIMVS and FSIMVS, respectively. First, we decompose an image into non-overlapping patches, calculate visual saliency, and assign a parameter ranging from 0 and 1 to each patch. Then, the local SSIM or FSIM values of the patches are weighed by the said parameter. Finally, the weighed similarity of all patches are integrated into one single index for the whole image. Experimental results are given to demonstrate the improved performance of the proposed VS-enhanced indices.