Examining Autoexposure for Challenging Scenes
Autoexposure (AE) is a critical step cameras apply to ensure properly exposed images. While current AE algorithms are effective in well-lit environments with unchanging 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 platforms to evaluate AE algorithms and suitable image datasets. To address this issue, we have designed a software platform allowing AE algorithms to be used in a plug-and-play manner with the dataset. In addition, we have captured a new 4D exposure dataset that provides a complete solution space (i.e., all possible exposures) over a temporal sequence with moving objects, bright lights, and varying lighting. Our dataset and associate platform enable repeatable evaluation of different AE algorithms and provide a much-needed starting point to develop better AE methods.
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