An enhanced image quality assessment by synergizing superpixels and visual saliency

Abstract

Superpixel and saliency-based evaluation methods play important roles in full reference image quality assessment (FR IQA). However, we find that these methods have one complementary principle and three limitations: (1) the weighted maps of superpixel-based methods conflict with the perception of the human visual system; (2) saliency-based methods are inefficient in terms of the block distortion; (3) the general two-direction gradient extraction factor must be extended to be multidirectional. To address these limitations, we propose an enhanced image quality assessment by synergizing superpixels and visual saliency. Specifically, the calculation of a newly proposed framework involves three similarities and two strategies: the saliency, superpixel and multidirectional gradient similarities of the neighborhoods, and the saliency pooling strategy, the fusion strategy of these similarities. Theoretical analysis and experimental results show that the proposed method can effectively address the limitations noted above and outperform the existing methods.