Exposure Compensation via Masked Reference Image Averaging
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Solution Overview
Problem
Existing image and video capture systems face challenges in maintaining consistent exposure settings over time, leading to inconsistencies in image quality due to automatic adjustments, which can hinder the effectiveness of video processing algorithms when exposure settings are unknown or varying.
Innovation Solution
A method that generates a masked reference image by applying a mask to exclude changing objects and calculates exposure adjustment estimates by averaging pixel value differences between corresponding pixels in two images, allowing for compensation of exposure adjustments to ensure consistent image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If automatic exposure adjustments are made between frames, then image quality is improved under varying lighting conditions, but consistency of exposure representation over time deteriorates
Solution Approach 1:
The system performs preliminary actions by capturing a reference frame and calculating exposure adjustments before processing subsequent frames. The exposure adjustment is computed based on the reference frame and applied to compensate for lighting changes, ensuring consistent exposure representation across frames while allowing automatic exposure adjustments to improve image quality under varying conditions.
2Adaptability or versatility
If digital exposure correction is applied dynamically, then adaptability to changing scenes is improved, but processing complexity increases
Solution Approach 1:
The processing is segmented into distinct stages: capturing a reference frame, calculating exposure adjustment based on the reference frame, and applying the adjustment to subsequent frames. This segmentation allows the system to adapt to changing scenes through dynamic exposure correction while managing processing complexity by breaking down the task into manageable, repetitive operations that can be efficiently implemented.
3Speed
If exposure settings vary between frames, then responsiveness to lighting changes is improved, but reliability of video processing algorithms deteriorates
Solution Approach 1:
The system uses feedback by continuously monitoring lighting conditions and comparing them to the reference frame, then adjusting exposure settings accordingly. This feedback mechanism allows the system to respond quickly to lighting changes while maintaining reliability of video processing algorithms by ensuring that exposure adjustments are systematically applied based on measured differences from the reference frame.
Data Source
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AI summary
Unknown exposure differences between two images of the same scene are compensated based on the common image portion: an compensation value is estimated by averaging, from the pixels of the first image having a particular pixel value in the common image portion, the differences between said particular pixel value and value of correpsonding pixels in the second image. The images may be in any color space for visible light, or images not of visible light, such as infrared or depth images.