The quality of acquired images can be surely reduced by improper exposures. Thus, in many vision-related industries, such as imaging sensor manufacturing and video surveillance, an approach that can routinely and accurately evaluate exposure levels of images is in urgent need. Taking an image as input, such a method is expected to output a scalar value, which can represent the overall perceptual exposure level of the examined image, ranging from extremely underexposed to extremely overexposed. However, studies focusing on image exposure level assessment (IELA) are quite sporadic. It should be noted that blind NR-IQA (no-reference image quality assessment) algorithms or metrics used to measure the quality of contrast-distorted images cannot be used for IELA. The root reason is that though these algorithms can quantify quality distortion of images, they do not know whether the distortion is due to underexposure or overexposure. This paper aims to resolve the issue of IELA to some extent and contributes to two aspects. Firstly, an Image Exposure Database (IE_psD) is constructed to facilitate the study of IELA. IE_psD comprises 24,500 images with various exposure levels, and for each image a subjective exposure score is provided, which represents its perceptual exposure level. Secondly, as IELA can be naturally formulated as a regression problem, we thoroughly evaluate the performance of modern deep CNN architectures for solving this specific task. Our evaluation results can serve as a baseline when the other researchers develop even more sophisticated IELA approaches. To facilitate the other researchers to reproduce our results, we have released the dataset and the relevant source code at https://cslinzhang.github.io/imgExpo/.

中文翻译：

---

走向自动图像曝光量评估

曝光不当必定会降低所获取图像的质量。因此，在许多与视觉有关的行业中，例如成像传感器制造和视频监控，迫切需要一种能够例行且准确地评估图像曝光水平的方法。以图像为输入，期望这种方法输出标量值，该标量值可以表示所检查图像的总体感知曝光水平，范围从极度曝光不足到极度曝光过度。但是，专注于图像曝光水平评估（IELA）的研究很少。应当注意，用于测量对比度失真图像质量的盲NR-IQA（无参考图像质量评估）算法或度量标准不能用于IELA。根本原因是，尽管这些算法可以量化图像的质量失真，他们不知道失真是由于曝光不足还是曝光过度引起的。本文旨在在一定程度上解决IELA问题，并从两个方面做出贡献。首先，一个图像曝光数据库（IE_ps D）旨在促进IELA的研究。IE _ps D包括24,500张具有各种曝光水平的图像，并且为每个图像提供了一个主观的曝光分数，代表其感知的曝光水平。其次，由于IELA可以自然地表述为回归问题，因此我们将彻底评估现代深度CNN架构用于解决此特定任务的性能。当其他研究人员开发更复杂的IELA方法时，我们的评估结果可以作为基准。为了方便其他研究人员重现我们的结果，我们在https://cslinzhang.github.io/imgExpo/上发布了数据集和相关的源代码。