Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement

TL;DR

This paper introduces a comprehensive framework for evaluating and optimizing low-light image enhancement based on perceptual quality, including a new dataset, an objective quality measure, and an integrated enhancement approach.

Contribution

It presents a large-scale subjective quality dataset, a novel objective quality assessment measure, and demonstrates how to optimize enhancement models for better perceptual quality.

Findings

The proposed quality measure accurately predicts perceptual quality.

Incorporating the measure improves enhancement model performance.

The dataset and code are publicly available for research use.

Abstract

There is a growing consensus in the research community that the optimization of low-light image enhancement approaches should be guided by the visual quality perceived by end users. Despite the substantial efforts invested in the design of low-light enhancement algorithms, there has been comparatively limited focus on assessing subjective and objective quality systematically. To mitigate this gap and provide a clear path towards optimizing low-light image enhancement for better visual quality, we propose a gap-closing framework. In particular, our gap-closing framework starts with the creation of a large-scale dataset for Subjective QUality Assessment of REconstructed LOw-Light Images (SQUARE-LOL). This database serves as the foundation for studying the quality of enhanced images and conducting a comprehensive subjective user study. Subsequently, we propose an objective quality assessment measure that plays a critical role in bridging the gap between visual quality and enhancement. Finally, we demonstrate that our proposed objective quality measure can be incorporated into the process of optimizing the learning of the enhancement model toward perceptual optimality. We validate the effectiveness of our proposed framework through both the accuracy of quality prediction and the perceptual quality of image enhancement. Our database and codes are publicly available at https://github.com/Baoliang93/IACA_For_Lowlight_IQA.