Examining Autoexposure for Challenging Scenes
Abstract
Autoexposure (AE) is a critical step applied by camera systems to ensure properly exposed images. While current AE algorithms are effective in well-lit environments with constant illumination, these algorithms still struggle in environments with bright light sources or scenes with abrupt changes in lighting. A significant hurdle in developing new AE algorithms for challenging environments, especially those with time-varying lighting, is the lack of suitable image datasets. To address this issue, we have captured a new 4D exposure dataset that provides a large solution space (i.e., shutter speed range from 1/500 to 15 seconds) over a temporal sequence with moving objects, bright lights, and varying lighting. In addition, we have designed a software platform to allow AE algorithms to be used in a plug-and-play manner with the dataset. Our dataset and associate platform enable repeatable evaluation of different AE algorithms and provide a much-needed starting point to develop better AE methods. We examine several existing AE strategies using our dataset and show that most users prefer a simple saliency method for challenging lighting conditions.
Cite
Text
Tedla et al. "Examining Autoexposure for Challenging Scenes." International Conference on Computer Vision, 2023. doi:10.1109/ICCV51070.2023.01202Markdown
[Tedla et al. "Examining Autoexposure for Challenging Scenes." International Conference on Computer Vision, 2023.](https://mlanthology.org/iccv/2023/tedla2023iccv-examining/) doi:10.1109/ICCV51070.2023.01202BibTeX
@inproceedings{tedla2023iccv-examining,
title = {{Examining Autoexposure for Challenging Scenes}},
author = {Tedla, SaiKiran and Yang, Beixuan and Brown, Michael S.},
booktitle = {International Conference on Computer Vision},
year = {2023},
pages = {13076-13085},
doi = {10.1109/ICCV51070.2023.01202},
url = {https://mlanthology.org/iccv/2023/tedla2023iccv-examining/}
}