S-CCR: Super-Complete Comparative Representation for Low-Light Image Quality Inference In-the-wild

With the rapid development of weak-illumination imaging technology, low-light images have brought new challenges to quality of experience and service. However, developing a robust quality indicator for authentic low-light distortions in-the-wild remains a major challenge in practical quality control systems. In this paper, we develop a new super-complete comparative representation (S-CCR) for the region-level quality inference of low-light images. Specifically, we excavate the color, luminance, and detail quality evidence for the feature embedding guidance of comparative representation based on the human visual characteristics. Moreover, we decompose the inputs into a super-complete feature group so that the image quality of each region can be fully represented, which allows to preserve the distinctiveness, distinguishability, and consistency. Finally, we further establish a comparative domain alignment method, so that the comparative representation of an unseen image can be aligned with respect to the quality features of already-seen ones. Extensive experiments on the benchmark dataset validate the superiority of our S-CCR over 11 competing methods on authentic distortions.