Saliency-based image quality assessment metric

Abstract

Traditional image quality assessment (IQA) metrics are most based on the discrepancy between reference image and the distorted image does not correlate well with the perceptive mechanism of human visual system (HVS). In this paper, we found from the experimental investigations that perceptible quality distortions can lead to some measurable changes in the visual saliency (VS) map of the image, and then proposed a novel and effective full reference image quality assessment method by means of VS. In our method, VS serves two functions which one is as a feature for computing the distorted image's local quality map and the other is used for a weight to reflect the importance of a local region when pooling. However, VS map alone sometimes does not work quite well when image's contrast change or color distortion, so we also merged the gradient similarity map and chrominance similarity map into the IQA. We named our proposed IQA metric as visual saliency-based metric (VSM). The experiments carried out on a benchmark datasets demonstrate that our proposed VSM outperform most of the state-of-the-art IQA metrics in terms of the prediction accuracy.