Image Processing White Balance Gain Update Method
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Solution Overview
Problem
Digital cameras face difficulties in automatic white balance due to differences in characteristics and sensitivity between human eyes and image sensors, leading to unrealistic color casts in images.
Innovation Solution
An image processing method that forms reference division lines on a GRGB grid, extracts red and blue gain values, determines the maximum pixel distribution point, and updates gain values using weighted values based on color temperature, employing a Macbeth chart for accurate white balance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional auto white balance is used, then the process is simple, but color casts occur due to differences between human eyes and image sensors
Solution Approach 1:
The patent segments the white balance processing by dividing the gain value calculation into multiple stages: initial gain calculation, pixel distribution analysis, and iterative refinement. The reference division line is segmented into multiple points, and each point is evaluated separately to determine the maximum pixel distribution, enabling precise color correction while maintaining systematic processing.
Solution Approach 2:
The patent implements feedback mechanisms by iteratively updating gain values based on pixel distribution analysis. The process calculates initial gain values, analyzes pixel distribution at reference points, determines maximum distribution points, and updates gain values accordingly. This closed-loop feedback continues until convergence, ensuring accurate white balance correction.
2Manufacturing precision
If gain values are adjusted to correct color casts, then image quality improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing the reference division line on the GRGB grid and pre-calculating initial gain values before the main white balance adjustment. This preliminary setup includes defining reference points and computing initial red and blue gain values, which accelerates the subsequent iterative refinement process by having ready-made starting points.
Solution Approach 2:
The patent employs dynamic processing by adaptively adjusting the number of iterations and refinement levels based on the specific image characteristics. The algorithm dynamically determines when to stop iterating by checking if the maximum pixel distribution point has been identified, allowing flexible processing time allocation based on image complexity.
3Measurement precision
If multiple reference points are analyzed for gain calculation, then white balance accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific reference points along the division line rather than uniformly processing all pixels. The algorithm identifies the maximum pixel distribution point locally and concentrates refinement efforts around this point, achieving high color accuracy while reducing overall computational complexity by avoiding unnecessary calculations in low-relevance areas.
Data Source
AI summary
Disclosed is an image processing method. The image processing method includes forming a reference division line to perform white balance, forming a grid including a pixel distribution degree based on the reference division line, extracting a red gain value, a blue gain value, and the pixel distribution degree with respect to a specific point on the reference division line, determining if the extracted pixel distribution degree is a maximum point, and setting new gain values by using the extracted red gain value and the extracted blue gain value depending on if the pixel distribution degree is the maximum point.