Structural similarity index (SSIM) revisited: A data-driven approach

Several contemporaneous image processing and computer vision systems rely upon the full-reference image quality assessment (IQA) measures. The single-scale structural similarity index (SS-SSIM) is one of the most popular measures, and it owes its success to the mathematical simplicity, low computational complexity, and implicit incorporation of Human Visual System's (HVS) characteristics. In this paper, we revise the original parameters of SSIM and its multi-scale counterpart (MS-SSIM) to increase their correlation with subjective evaluation. More specifically, we exploit the evolutionary computation and the swarm intelligence methods on five popular IQA databases, two of which are dedicated distance-changed databases, to determine the best combination of parameters efficiently. Simultaneously, we explore the effect of different scale selection approaches in the context of SS-SSIM. The experimental results show that with a proper fine-tuning (1) the performance of SS-SSIM and MS-SSIM can be improved, in average terms, by 8% and by 3%, respectively, (2) the SS-SSIM after the so-called standard scale selection achieves similar performance as if applying computationally more expensive state-of-the-art scale selection methods or MS-SSIM; moreover, (3) there is evidence that the parameters learned on a given database can be successfully transferred to other (previously unseen) databases; finally, (4) we propose a new set of reference parameters for SSIM's variants and provide their interpretation.