Rethinking No-reference Image Exposure Assessment from Holism to Pixel: Models, Datasets and Benchmarks

The past decade has witnessed an increasing demand for enhancing image quality through exposure, and as a crucial prerequisite in this endeavor, Image Exposure Assessment (IEA) is now being accorded serious attention. However, IEA encounters two persistent challenges that remain unresolved over the long term: the accuracy and generalizability of No-reference IEA are inadequate for practical applications; the scope of IEA is confined to qualitative and quantitative analysis of the entire image or subimage, such as providing only a score to evaluate the exposure level, thereby lacking intuitive and precise fine-grained evaluation for complex exposure conditions. The objective of this paper is to address the persistent bottleneck challenges from three perspectives: model, dataset, and benchmark.