No-reference quality assessment for low-light image enhancement: Subjective and objective methods
Weitao Lin, Yuxuan Wu, Lishi Xu, Weiling Chen, Tiesong Zhao, Hongan Wei
Abstract
No-Reference (NR) quality assessment is a crucial approach for evaluating the quality of low-light enhanced images, as it is often difficult to acquire high-quality reference images in applications such as night-time automatic driving. However, current NR evaluation methods for low-light enhanced images often lack consideration of important characteristics such as color, structure, and naturalness. This paper proposes a novel NR quality assessment method for NR low-light enhanced images from both subjective and objective aspects. On the subjective side, we construct the Low-light Enhanced Images Subjective Dataset (LEISD) containing 2040 images with 255 different image contents . Each image was evaluated based on the Single Stimulus (SS) method by 20 subjects. On the objective side, we propose Multi-Features Reconciliation-based Quality Assessment (MFRQA) methods for low-light enhanced images by observing the low-light enhanced images. The MFRQA summarized four key feature perspectives: brightness, color, structure and naturalness, and employed the traditional machine learning model to reconcile the multi-features. Experimental results on the LEISD dataset demonstrate competitive performance and low complexity of our method compared to the representative quality metrics.