Examining Autoexposure for Challenging Scenes

TL;DR

This paper introduces a new 4D exposure dataset and platform to evaluate autoexposure algorithms in challenging, time-varying lighting environments, revealing preferences for simple saliency-based methods.

Contribution

The paper provides a novel dataset and software platform for evaluating autoexposure algorithms in complex lighting conditions, facilitating future research.

Findings

Most users prefer simple saliency-based AE methods in challenging scenes.

Existing AE algorithms struggle with abrupt lighting changes.

The dataset enables repeatable and comprehensive evaluation of AE strategies.

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.