Examining Autoexposure for Challenging Scenes
SaiKiran Tedla Beixuan Yang Michael S. Brown
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.