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There is an increasing consensus that the design and optimization of lowlight image enhancement methods need to be fully driven by perceptual quality.With numerous approaches proposed to enhance low-light images, much less workhas been dedicated to quality assessment and quality optimization of low-lightenhancement. In this paper, to close the gap between enhancement andassessment, we propose a loop enhancement framework that produces a clearpicture of how the enhancement of low-light images could be optimized towardsbetter visual quality. In particular, we create a large-scale database forQUality assessment Of The Enhanced LOw-Light Image (QUOTE-LOL), which serves asthe foundation in studying and developing objective quality assessmentmeasures. The objective quality assessment measure plays a critical bridgingrole between visual quality and enhancement and is further incorporated in theoptimization in learning the enhancement model towards perceptual optimally.Finally, we iteratively perform the enhancement and optimization tasks,enhancing the low-light images continuously. The superiority of the proposedscheme is validated based on various low-light scenes. The database as well asthe code will be available.

The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement