Full-reference image quality metrics (FR-IQMs) aim to measure the visual differences between a pair of reference and distorted images, with the goal of accurately predicting human judgments. However, existing FR-IQMs, including traditional 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 of human perception. In this work, rather than devising a novel IQM model, we seek to improve upon the perceptual quality of existing FR-IQM methods. We achieve this by considering visual masking, an important characteristic of the human visual system that changes its sensitivity to distortions as a function of local image content. Specifically, for a given FR-IQM metric, we propose to predict a visual masking model that modulates reference and distorted images in a way that penalizes the visual errors based on their visibility. Since the ground truth visual masks are difficult to obtain, we demonstrate how they can be 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-IQM metrics that are more in line with human prediction both visually and quantitatively.

翻译：全参考图像质量指标（FR-IQMs）旨在衡量参考图像与失真图像对之间的视觉差异，以准确预测人类判断。然而，现有FR-IQM方法，包括传统指标如PSNR和SSIM，甚至感知指标如HDR-VDP、LPIPS和DISTS，仍难以捕捉人类感知的复杂性与细微差别。本研究并非提出新型IQM模型，而是寻求改进现有FR-IQM方法的感知质量。通过考虑视觉掩膜这一人类视觉系统的重要特性（该特性根据局部图像内容改变对失真的敏感度），我们针对特定FR-IQM指标提出预测视觉掩膜模型，该模型通过调整参考图像与失真图像，根据视觉误差的可见性对其进行惩罚。由于真实视觉掩膜难以获得，我们展示了如何仅基于从FR-IQM数据集收集的平均意见分数（MOS），以自监督方式推导出掩膜。我们的方法所增强的FR-IQM指标，在视觉与量化层面均更符合人类预测。