Multi-Camera Image Quality Adjustment via Confidence Levels
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Solution Overview
Problem
Existing techniques for adjusting image quality across multiple cameras either require manual intervention, leading to quality fluctuations, or independent automatic adjustments that result in inconsistent image quality among cameras.
Innovation Solution
An information processing device with an adjustment value determination unit that calculates a second image quality adjustment value for each camera based on a first adjustment value and a confidence level, ensuring uniformity and accuracy across multiple cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual image quality adjustment is performed for each camera, then the user can control the adjustment process, but the adjustment accuracy decreases and quality fluctuations occur
Solution Approach 1:
The system enables cameras to automatically determine their own image quality adjustment values based on captured images and confidence levels, eliminating the need for manual adjustment while maintaining high accuracy. Each camera independently evaluates its image quality and self-adjusts parameters such as white balance, gamma correction, and noise reduction.
Solution Approach 2:
The system implements a feedback mechanism where adjustment values are determined based on confidence levels calculated from captured images. The adjustment value determination unit uses this feedback to iteratively refine adjustment parameters, ensuring high accuracy while maintaining operational simplicity.
2Ease of operation
If independent automatic adjustment is performed for each camera, then operational simplicity is improved, but image quality consistency across cameras deteriorates
Solution Approach 1:
The system merges the adjustment processes of multiple cameras by having the adjustment value determination unit collectively determine adjustment values for all cameras based on their respective captured images and confidence levels. This ensures that while each camera adjusts independently, the adjustments are coordinated to maintain consistency across the camera array.
Solution Approach 2:
The system dynamically changes adjustment parameters (white balance, gamma correction, noise reduction) for each camera based on calculated confidence levels. By adjusting these parameters proportionally to confidence levels, the system maintains image quality consistency across cameras while adapting to individual camera conditions.
3Stability of the object's composition
If uniform adjustment values are applied to all cameras, then image quality consistency is improved, but adaptability to individual camera conditions deteriorates
Solution Approach 1:
The system applies the principle of local quality by determining adjustment values individually for each camera based on its specific captured image and confidence level. Instead of applying a uniform adjustment to all cameras, each camera receives customized adjustment parameters that are locally optimized for its conditions while maintaining overall consistency through the coordinated determination process.
Data Source
AI summary
Provided is an information processing device including an adjustment value determination unit that determines a second image quality adjustment value of each of a plurality of cameras on a basis of a first image quality adjustment value of each of the plurality of cameras and a confidence level of each of the plurality of cameras that are acquired from each of the plurality of cameras.