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A No-Reference Evaluation Metric for Low-Light Image Enhancement

Low-light images, which are usually taken in dark or back-lighting conditions, are hard to perceive due to the low visibility and low contrast. To improve viewers’ Quality of Experience (QoE) and support the application of vision-based systems, various low-light image enhancement algorithms (LIEAs) have been proposed to lighten low-light images. However, some LIEAs may amplify the hidden distortions in the dark like noise and even further, introduce new distortions such as structural damage, color shift, etc, which severely affect the quality of light-enhanced images and need to be evaluated quantificationally. However, in the literature, few measures are proposed to assess the quality of light-enhanced images. Therefore, in this paper, we develop a no-reference low-light image enhancement evaluation (NLIEE) metric to predict the quality of light-enhanced images. The image quality is mainly assessed from four key aspects: light enhancement, color comparison, noise measurement, and structure evaluation. The experiment results show that NLIEE achieves the best performance among the general no-reference image quality assessment (NR IQA) models and quality descriptors for light enhancement.