Joint luminance-chrominance learning for quality assessment of low-light image enhancement

Existing methods for low-light enhancement quality assessment (LEQA) often underperform across diverse scenarios. One reason is that most of them rely on shallow feature respresentations, while another is that deep-learning-based counterparts fail to make full use of the unique characteristics of low-light enhanced images (LEIs), such as luminance enhancement and color refinement. In this paper, we propose a novel Joint Luminance-Chrominance Learning Network (JLCLNet) for LEQA to comprehensively assess the effects of low-light image enhancement (LLIE) algorithms. Specifically, we construct a two-branch network architecture consisting of a luminance learning branch and a chrominance learning branch. In the luminance learning branch, the low- and high-frequency subbands of the luminance channel in the CIELAB color space, derived from the dual-tree complex wavelet transform (DTCWT), focus on measuring contrast enhancement and structure preservation. Meanwhile, the chrominance learning branch addresses potential color distortions by integrating perceptual information from the two parallel chrominance channels of the CIELAB color space. Finally, the complementary features from both branches are fused to predict quality scores. Experimental results on four public LEQA databases demonstrate the performance advantages of the proposed method compared to the state-of-the-art approaches. The source code of JLCLNet is available at https://github.com/li181119/JLCLNET.