Abstract
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 (IEpsD) is constructed to facilitate the study of IELA. IEpsD 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/.
Highlights
Exposure is the total amount of light falling on a photographic medium when capturing an image [1]
Test Protocol. e collected dataset IEpsD_R was used to evaluate the approaches’ capability for predicting the image’s perceptual exposure level. e performance of representative blind NR image quality assessment (NR-IQA) models, QA models for contrast-distorted images, and models specially designed for image exposure level assessment (IELA) was thoroughly studied and analyzed
Four widely accepted metrics are adopted to evaluate the performance of the competing methods. e first two are the Spearman rank-order correlation coefficient (SROCC) and the Kendall rank-order correlation coefficient (KROCC)
Summary
Exposure is the total amount of light falling on a photographic medium when capturing an image [1]. A method that can accurately assess the exposure levels of images is in urgent need [2,3,4,5]. When the user is taking images with this mode, the camera will automatically adjust relevant hardware parameters (such as the aperture, the shutter speed, and the electronic gain [6]) using a particular autoexposure algorithm to make the collected images have proper exposure levels. In order to verify the performance of an autoexposure algorithm, a method that can accurately assess the exposure levels of acquired images is indispensable. Another example commonly encountered is in video surveillance. It is quite necessary to continuously monitor the exposure level of the acquired video to determine its quality [4]
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