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Full-reference image quality metrics (FR-IQMs) aim to measure the visualdifferences between a pair of reference and distorted images, with the goal ofaccurately predicting human judgments. However, existing FR-IQMs, includingtraditional ones like PSNR and SSIM and even perceptual ones such as HDR-VDP,LPIPS, and DISTS, still fall short in capturing the complexities and nuances ofhuman perception. In this work, rather than devising a novel IQM model, we seekto improve upon the perceptual quality of existing FR-IQM methods. We achievethis by considering visual masking, an important characteristic of the humanvisual system that changes its sensitivity to distortions as a function oflocal image content. Specifically, for a given FR-IQM metric, we propose topredict a visual masking model that modulates reference and distorted images ina way that penalizes the visual errors based on their visibility. Since theground truth visual masks are difficult to obtain, we demonstrate how they canbe derived in a self-supervised manner solely based on mean opinion scores(MOS) collected from an FR-IQM dataset. Our approach results in enhanced FR-IQMmetrics that are more in line with human prediction both visually andquantitatively.

Enhancing image quality prediction with self-supervised visual masking