Studying the added value of computational saliency in objective image quality assessment
Advances in image quality assessment have shown the potential added value of including visual attention aspects in objective quality metrics. Numerous models of visual saliency are implemented and integrated in different quality metrics; however, their ability of improving a metric's performance in predicting perceived image quality is not fully investigated. In this paper, we conduct an exhaustive comparison of 20 state-of-the-art saliency models in the context of image quality assessment. Experimental results show that adding computational saliency is beneficial to quality prediction in general terms. However, the amount of performance gain that can be obtained by adding saliency in quality metrics highly depends on the saliency model and on the metric.