Gradient-weighted structural similarity for image quality assessments
Qiaohong Li, Yuming Fang, Weisi Lin, Daniël Thalmann · 2015
The goal of Image Quality Assessment (IQA) is to design computational models that can automatically predict the perceived image quality consistent with human subjective ratings. In this paper, we propose a full reference IQA metric gradient weighted structural similarity (GW-SSIM) by incorporating the gradient information to the well-known IQA metric SSIM. Experimental results demonstrate that GW-SSIM can greatly improve the quality prediction accuracy and achieve the best performance among the SSIM-based methods by addressing SSIM's shortcomings. Additionally, incorporating the proposed gradient weighting (GW) map into peak-signal-to-noise ratio (PSNR) also makes it quite competitive to state-of-the-art IQA models, and this is meaningful since PSNR is still a widely adopted metric.