A Metric For Quantifying Image Quality Induced Saliency Variation

Saliency plays an important role in the area of image quality assessment. Image distortions cause shift/redistribution of saliency from its original places. There is a need to be able to measure such distortion-included saliency variation (DSV), so that the use of saliency can be optimised for automated image quality assessment. Effort has been made in our previous study to build a benchmark for the measurement of DSV through subjective testing. In this paper, we demonstrate that exiting similarity measures are unhelpful for the quantification of DSV. Thus, we propose a new metric for DSV combining local and global measures using convex optimization. The experimental results show that our proposed metric can accurately quantify saliency variation.