{Amine Bermak Pedro V. Sander Bo Zhang}

Abstract

Recently, various image quality assessment (IQA) metrics based on gradient similarity have been developed. In this paper, we extend the work of gradient magnitude similarity deviation (GMSD) and propose a more efficient metric. First, a novel similarity index is proposed, which gives the flexibility to tune the masking parameter to more closely match the human vision system (HVS). Then, we propose a multi-scale GMSD method by incorporating scores of luminance distortion at different scales. Furthermore, a method for measuring chromatic distortions in YIQ color space based on our metric is proposed. The final IQA index, MS-GMSD c , is obtained by combining luminance and chrominance scores. Experimental results on four comprehensive datasets clearly show that, compared with 14 state-of-the-art IQA methods, our method achieves the best performance for both grayscale and chromatic image assessment.