Self-Calibrating HDR Video Merging from Multi-Exposure Images
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Solution Overview
Problem
Existing imaging systems face inaccuracies in determining the exposure time ratio (ETR) due to delays and inaccuracies in auto-exposure controllers, leading to undesirable artifacts in merged high dynamic range (HDR) video frames.
Innovation Solution
A self-calibrating technique estimates the ETR using iterative linear regression on pixel pairs from images captured with different exposure times, independently of auto-exposure controller information, to accurately merge images and reduce artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If auto-exposure controller information is used to determine exposure time ratio, then merging process is simplified, but measurement precision deteriorates due to delays and inaccuracies
Solution Approach 1:
The system performs self-calibration by automatically determining the exposure time ratio through linear regression analysis of pixel pairs from images captured at different exposure times. This self-service mechanism eliminates dependence on potentially inaccurate auto-exposure controller information while maintaining processing automation.
Solution Approach 2:
The patent replaces the mechanical/controller-based exposure time ratio determination with a computational approach using linear regression analysis on pixel data. This substitution of computational method for controller-based method achieves higher precision without significantly increasing overall system complexity.
2Measurement precision
If self-calibrating technique with iterative linear regression is used, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system uses pixel pairs from captured images as data copies to perform linear regression analysis. By working with replicated pixel data rather than directly modifying the imaging hardware or control systems, the patent achieves precision improvement through software-based computation with minimal hardware complexity increase.
3Manufacturing precision
If images are merged using accurate ETR, then manufacturing precision improves, but loss of time occurs due to iterative regression process
Solution Approach 1:
The system performs preliminary linear regression analysis on pixel pairs to determine the exposure time ratio before executing the actual image merging process. This preliminary determination of accurate ETR parameters enables subsequent merging operations to proceed with high precision while optimizing the overall processing time through efficient parameter pre-calculation.
Data Source
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AI summary
A high dynamic range video frame can be generated from images captured using different exposure times. The merging of images may use a parameter, exposure time ratio, to combine pixel values of images to form a merged video frame. The quality of the merged video frame can depend on accuracy of the exposure time ratio. In some scenarios, the exposure time ratio is unknown or the reported information about the exposure time ratio from an auto-exposure controller is inaccurate. Using an inaccurate exposure time ratio to merge images would result in undesirable artifacts in the merged video frame. To address this issue, a self-calibrating technique may be implemented to derive the exposure time ratio based on the images themselves.