Structure Similarity for Image Quality Assessment from Isotropic to Anisotropic
Yuanyuan Chang, Xuande Zhang, Dekang Zhu · 2021
As a pioneer work in the field of Image Quality Assessment (IQA), Structure Similarity (SSIM) has been widely used to evaluate the output quality of imaging systems and algorithms. However, the classical implementation of SSIM is inherent of isotropic nature and thus fail to model the anisotropic characteristic of Human Vision System (HVS). To alleviate this problem and realize as fully as possible the potential of SSIM, an anisotropic implementation is put forward in this letter in which a kernel adaptive to image local structures is integrated in SSIM computation. Extensive Numerical experiments reveal that this seemingly tiny modification (from isotropic to anisotropic) leads to significant performance improvement, and the anisotropic implementation of SSIM performs comparably with state-of-the-art on several benchmark datasets.