Auto-Exposure Algorithm Based on Luminance Histogram and Region Segmentation

Abstract

This paper proposed an automatic exposure algorithm based on luminance histogram and region segmentation. The method ensures well exposure to the main object under the backlighting or excessive front-lighting conditions, and uses the characteristics of the luminance histogram to judge the scene condition. According to the luminance histogram, if the image under normal light conditions, the method compares the average luminance value of the image with the corresponding reference target brightness value to adjust exposure compensation. If the image under special conditions, the average brightness value will be calculated using fixed-partition method and exposure compensation will be adjusted. The paper combines auto-exposure and auto-gain control to adjust the exposure compensation. As it turns out, this method in a variety of light conditions can achieve well exposure.

Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning

“…However, assuming different options for meter mode are available, users may need to modify the meter mode (e.g., spot, matrix) and focus area for handling more challenging conditions such as back-lit scenes. Techniques for more specialized treatment of frontand back-lit scenes have been described (e.g., [8]), but they fundamentally rely on histogram analysis that does not jointly consider object importance and lighting condition. Some native cameras on popular phones prioritize exposure on detected face regions [9], [10], [11]; many allow user interaction to select a region of interest for appropriate exposure adjustment.…”