Image Exposure Assessment: A Benchmark and a Deep Convolutional Neural Networks Based Model

Abstract

In the camera equipment manufacturing industry, the exposure calibration is one of the basic steps for manufacturers to consider before launching their products to the market. To this end, a method that can objectively and automatically assess the exposure levels of images taken by the camera is highly desired. However, few studies have been conducted in this area. In this paper, we attempt to solve this issue to some extent and our contributions are twofold. Firstly, in order to facilitate the study of image exposure assessment, an Image Exposure Database $(IE_{ps}D)$ is established. In this database, there are 15, 582 images with various exposure levels, and for each image there is an associated subjective exposure score which could reflect its perceptual exposure level. Secondly, we propose a novel highly accurate DCNN-based model, namely $IE_{ps}M$ (Image Exposure Metric), to predict the exposure level of a given image.