A Measurement for Distortion Induced Saliency Variation in Natural Images

How best to measure spatial saliency shift induced by image distortions is an open research question. Our previous study has shown that image distortions cause saliency to deviate from their original places in natural images, and the degree of such distortion-induced saliency variation (DSV) depends on image content and the properties of distortion. Being able to measure DSV benefits the development of saliency-based image quality algorithms. In this article, we first investigate the plausibility of using existing mathematical algorithms for measuring DSV and their potential limitations. We then develop a new algorithm for quantifying DSV based on a deep neural network. In the algorithm, namely, saliency similarity transformation DSV (ST-DSV), we design a coarse-grained to fine-grained saliency similarity transformation approach to achieve DSV measurement. The experimental results show that the proposed ST-DSV algorithm significantly outperforms existing methods in predicting the ground truth DSV.