Studying The Added Value of Visual Attention in Objective Image Quality Metrics

In this work, we investigate the benefits of incorporating saliency maps obtained with visual attention computational models into three image quality metrics. In particular, we compare the performance of simple quality metrics with quality metrics that incorporate saliency maps obtained using three popular visual attention computational models. Results show that performance of simple quality metrics can be improved by adding visual attention information. Nevertheless, gains in performance depend on the precision of the visual attention model, the type of distortion, and the characteristics of the quality metric. Keywords-image quality, ssim, degradations, image processing