Linking visual saliency deviation to image quality degradation: A saliency deviation-based image quality index

Abstract

Advances in image quality research have shown the benefits of modeling functional components of the human visual system in image quality metrics. Recently, visual saliency, an important aspect of the human visual system, is increasingly investigated in relation to visual quality perception. Existing studies have showed that incorporating visual saliency leads to improved performance of image quality metrics. However, current applications of visual saliency in image quality metrics mainly focus on the extension of a specific metric with a specific visual saliency model. Issues regarding the optimal use of visual saliency in image quality metrics remain. Psychophysical experiments conducted in the literature have revealed that visual artifacts occurring in an image can change fixation deployment relative to that of the image without distortion. As such, instead of using saliency models as add-ons to image quality metrics, we explored the approach of directly assessing image quality by measuring the visual saliency deviation triggered by visual artifacts. We first analyzed the relationship between visual saliency deviation and image quality degradation on the basis of a large-scale eye-tracking dataset. A saliency deviation-based image quality index was then devised. Experimental results showed that the proposed metric features high prediction accuracy and relatively low computational cost.