Improved SSIM IQA of contrast distortion based on the contrast sensitivity characteristics of HVS

Abstract

Currently, the structural similarity index metric (SSIM) is recognised generally and applied widely in image quality assessment (IQA). However, using SSIM to evaluate contrast-distorted images from TID2013 and CSIQ databases is low effective. In this study, the authors improve SSIM for contrast-distorted images by combining it with the contrast sensitivity characteristics of human visual system (HVS). In the improved method, first, they combine the visual characteristics to propose a model that HVS perceives the real image. Then, this model is used to eliminate the visual redundancy of real images. Afterwards, the perceived images are evaluated using SSIM. Furthermore, 241 contrast-distorted images from TID2013 and CSIQ databases were used in experiments. The results have shown that in comparison with SSIM scores, the scores obtained by the improved SSIM are more consistent with the subjective assessment scores. Moreover, the Pearson linear correlation coefficient and Spearman rank order correlation coefficient between subjective and objective scores are averagely improved by 12.83 and 22.78%, respectively. In addition, the assessment accuracy of the improved SSIM is better than that of five commonly used IQA metrics. Also, it has an excellent generalisation performance. These results show that the assessment performance of the improved SSIM is effectively enhanced.