@inproceedings{
he2024rethinking,
title={Rethinking No-reference Image Exposure Assessment from Holism to Pixel:  Models, Datasets and Benchmarks},
author={Shuai He and Shuntian Zheng and Anlong Ming and Banyu Wu and Huadong Ma},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=zVrQeoPIoQ}
}
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.  1) Model-level: we propose a Pixel-level IEA Network (P-IEANet) that utilizes Haar discrete wavelet transform (DWT) to analyze, decompose, and assess exposure from both lightness and structural perspectives, capable of generating pixel-level assessment results under no-reference scenarios. 2) Dataset-level: we elaborately build an exposure-oriented dataset, IEA40K, containing 40K  images, covering 17 typical lighting scenarios, 27 devices, and 50+ scenes, with each image densely annotated by more than 10 experts with pixel-level labels.  3) Benchmark-level: we develop a comprehensive benchmark of 19 methods based on IEA40K. Our P-IEANet not only achieves state-of-the-art (SOTA) performance on all metrics but also seamlessly integrates with existing exposure correction and lighting enhancement methods. To our knowledge, this is the first work that explicitly emphasizes assessing complex image exposure problems at a pixel level, providing a significant boost to the IEA and exposure-related community. The code and dataset are available in \href{https://github.com/mRobotit/Pixel-level-No-reference-Image-Exposure-Assessment}{\textcolor{red} {here}}.
Rethinking No-reference Image Exposure Assessment from Holism to Pixel:  Models, Datasets and Benchmarks
Shuai He Shuntian Zheng Anlong Ming Banyu Wu Huadong Ma