Abstract: Low-light image enhancement improves the perception and interpretability of the images,and the assessment of the enhanced images impacts the image’s reliability and playing a guiding role in parameter selection and model adjustment of the enhancement algorithm.However,the existing image quality assessments are not completely for low-light enhanced images,which lead to discrepancies between the assessment results and subjective perceptions.In this paper,a multi-criterion based Low-light Enhanced Image Quality Assessment(MC-LEIQA) is proposedby analyzing of the visual loss factors based on human visual perception.According to the visual artifacts such as insufficient brightness gain,artifacts,false contours,and color shifts that occur during the process of enhancing low-light images,MC-LEIQA designs an assessment criterion based on the fusion of adaptive brightness gain degree using Kullback-Leibler divergence,structural recovery degree based on variance and gradient,and color recovery degree.Additionally,it introduces a correction coefficient for positive offset that incorporates automatic brightness perception to achieve accurate quality assessment of low-light enhanced images.Ablation experiments demonstrate the rationality and necessity of the selected assessment metrics in this study.Furthermore,comparative experiments with the classical image quality assessment methods on public datasets further validate that the proposed method exhibits higher assessment accuracy and effectiveness for low-light enhanced images.

Key words: Low-light image enhancement, Image quality assessment, Multi-criteria fusion, Image visual loss analysis