Perceptual Quality Assessment of Low-Light Enhanced Images: A Multi-Annotated Subjective Dataset and a Multimodal...

Low-light Image Enhancement Algorithms (LIEAs) aim to improve the visibility and visual quality of images captured in low-light environments. However, none of the existing LIEAs can comprehensively restore all visual contents, which makes it inevitable for the Enhanced Low-light Images (ELIs) to have different degrees of distortion, thereby affecting the visual quality. Currently, there is little research focusing on the quality assessment of these ELIs, partly due to the lack of publicly available datasets. Moreover, existing quality assessment methods primarily focus on a single visual modality and fail to sufficiently exploit the structural information across multiple image attributes, consequently resulting in suboptimal prediction performance. To this end, this paper conducts a systematic study on both subjective and objective quality assessment of ELIs. Firstly, we construct the first Multi-annotated and multi-modal Low-light image Enhancement quality dataset (MLE), which contains 1,000 ELIs, along with subjective studies to obtain multiple attribute annotations, quality scores, and textual descriptions. Based on this, we further propose an Attribute-guided Vision-Language Graph Reasoning Network (AVGR-Net) for ELI quality prediction, which effectively integrates multi-attribute visual and textual information through cross-modal graph reasoning and alignment. Extensive data analysis and experimental results validate both the reliability of the MLE dataset and the superior performance of the AVGR-Net compared to state-of-the-art methods.