Image Quality Assessment based on Improved Structural SIMilarity ⋆

Abstract. In this paper, we propose a novel image quality assessment (IQA) based on an Improved Structural SIMilarity (ISSIM) which con-siders the spatial distributions of image structures. The existing struc-tural similarity (SSIM) metric, which measures structure loss based on statistical moments, i.e., the mean and variance, represents mainly the luminance change of pixels rather than describing the spatial distribu-tion. However, the human visual system (HVS) is highly adapted to extract structures with regular spatial distributions. In this paper, we employ a self-similarity based procedure to describe the spatial distribu-tion of image structures. Then, combining with the statistical characters, we improve the structural similarity based quality metric. Furthermore, considering the viewing condition, we extend the ISSIM metric to the multi-scale space. Experimental results demonstrate the proposed IQA metric is more consistent with the human perception than the SSIM metric