LEIQ-Assessor: Multi-dimensional Quality Assessment of Low-light Enhanced Images via Multi-task Learning
2026-06-29 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMultimedia
AI summaryⓘ
The authors developed a tool called LEIQ-Assessor to judge how good low-light image enhancement is without needing a reference image. It uses a special vision transformer to look at various aspects like brightness, color accuracy, noise, exposure, naturalness, and content recovery all at once. This multi-task approach helps the model understand image quality better than methods focusing on just one thing. Their tests show it works better than previous methods and it performed well in a quality assessment challenge.
low-light image enhancementimage quality assessmentmulti-task learningVision TransformerMean Opinion Scoreperceptual attributesno-reference IQAPLCC lossMLE benchmarkQoMEX Grand Challenge
Authors
Wei Sun, Yanwei Jiang, Dandan Zhu, Jinqiu Sang, Jikai Xu, Weixia Zhang, Guangtao Zhai
Abstract
Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement process often introduces artifacts such as noise amplification, color shift, structural damage, and over-exposure, which degrade the perceptual quality of the enhanced images. Therefore, a reliable image quality assessment (IQA) metric for evaluating enhancement effects is of great importance for both the development of LIEAs and their practical applications. In this paper, we present \textbf{LEIQ-Assessor}, a multi-dimensional quality assessment model for low-light image enhancement based on multi-task learning, developed for the QoMEX 2026 Grand Challenge on Low-light Enhanced Image Quality Assessment. Specifically, our method leverages a pre-trained SigLIP2 Vision Transformer as the backbone and simultaneously predicts the overall Mean Opinion Score (MOS) together with six perceptual sub-attributes: lightness, color fidelity, noise level, exposure quality, naturalness, and content recovery. By jointly optimizing these correlated objectives via the PLCC loss, the shared representation captures richer quality-aware features than its single-task counterpart. Experiments on the MLE benchmark demonstrate that LEIQ-Assessor significantly outperforms existing no-reference IQA models and hand-crafted quality descriptors. Our method achieved second place in the QoMEX 2026 Grand Challenge on Low-light Enhanced Image Quality Assessment. The code is available at https://github.com/sunwei925/LEIQ-Assessor.