Multi-Camera Color Balancing via Histogram Peak Detection
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Solution Overview
Problem
Overlapping cameras in multi-camera systems, such as videoconferencing endpoints, face challenges in color and exposure matching due to differing output colors, requiring time-consuming manual calibration and prone to drift, which can compromise image quality if not regularly recalibrated.
Innovation Solution
A method for color and exposure matching that uses geometric relationships between overlapping cameras to develop histograms, determine dynamic thresholds, and perform peak detection, allowing for incremental adjustments to align camera outputs, thereby simplifying later color and white balance blending.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual color balancing is performed on each camera, then color accuracy is improved, but calibration time and operational complexity increase significantly
Solution Approach 1:
The system performs automatic color balancing using histograms and peak detection algorithms, eliminating the need for manual calibration. The cameras self-adjust their color outputs by comparing histograms of overlapping regions and automatically applying correction factors, thereby resolving the contradiction between achieving color accuracy and reducing calibration time
Solution Approach 2:
The system changes the parameter being measured from individual pixel values to histogram distributions. By analyzing the statistical distribution of pixel intensities in overlapping regions and comparing peak positions, the system automatically determines color correction parameters, achieving both accuracy and efficiency
2Manufacturing precision
If manual color balancing is performed, then initial color matching is improved, but reliability deteriorates due to drift over time requiring frequent recalibration
Solution Approach 1:
The system implements continuous feedback by periodically analyzing histograms from overlapping camera regions and automatically adjusting color balance parameters. This closed-loop approach compensates for imager drift over time, maintaining color matching accuracy without requiring manual recalibration and thereby improving reliability
Solution Approach 2:
Instead of performing discrete manual recalibrations, the system continuously monitors histogram relationships and maintains color balance automatically. This continuous adjustment ensures stable color matching over time, resolving the reliability issue caused by drift
3Manufacturing precision
If one camera is designated as master and others are adjusted to it, then color consistency is improved, but the system becomes vulnerable to master camera drift compromising all outputs
Solution Approach 1:
The system segments the color balancing problem into pairwise comparisons between overlapping cameras rather than using a single master camera. Each camera pair independently determines their relative color relationship through histogram analysis, distributing the reference function across multiple cameras and eliminating the single point of failure associated with master camera drift
4Manufacturing precision
If detailed manual balancing is performed, then color accuracy is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system replaces manual mechanical adjustment procedures with automated computational processing. Histogram analysis and peak detection algorithms automatically determine color correction parameters, eliminating the need for operators to manually adjust each camera while achieving high precision color balancing
Data Source
AI summary
Color and exposure matching for systems, such as a videoconferencing endpoint, that have overlapping camera fields of view. The geometric relationships between the overlapping cameras are used to determine correction processing. For each camera, histograms are developed for the overlapping cameras. A dynamic threshold is determined for each histogram. Using the dynamic threshold, peak detection is performed on each histogram. Using the geometric relationships, expected histogram relationships are determined. The actual histogram relationships are compared to the expected relationships, with further processing based on the correctness of the comparison. In some of the cases of further processing, peaks of the histograms are compared to find matching and non-matching peaks. Various ratios of pixels in the various peaks are used to determine needed changes to respective cameras. Incremental changes to camera outputs are provided and accumulated so that overall changes can be provided to adjust the output of the respective cameras.