Incorporating Visual Attention Models into Image Quality Metrics

A recent development in the area of image quality consists of trying to incorporate aspects of visual attention in the metric design. This is generally achieved by combining distortion maps and saliency maps. Although a good number of saliency-inspired image quality metrics have been proposed, results are not yet conclusive. Some researchers have reported that the incorporation of visual attention increases the performance of quality metrics, while others have reported no or very little improvement. In this work, we investigate the benefit of incorporating saliency maps obtained with computational visual attention models into three image quality metrics (SSIM, PSNR, and MSE). More specifically, we compare the performance of quality metrics using saliency maps obtained with computational visual attention models versus quality metrics using subjective saliency maps. Results show that performance is linked to not only the “precision” of the saliency map, but also to the type of distortion.