Color Image Processing System RGB Adjustment Gaussian Distribution
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Solution Overview
Problem
Color images are prone to distortion due to differences in color channel sensitivity and light factors during digitization, leading to suboptimal display quality.
Innovation Solution
A color image processing system comprising an image capture module, digitizing module, optimizing ratio calculation module, and image processing module that calculates and adjusts RGB values based on specific ratios of color areas within Gaussian distributions to correct for distortions, ensuring accurate digitization and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If color images are digitized directly without correction, then the digitization process is simple and fast, but the color image quality deteriorates due to distortion from different color channel sensitivity and light factors
Solution Approach 1:
The patent applies preliminary action by pre-calculating optimizing ratios for different color channels based on Gaussian distribution characteristics before actual image processing. The system pre-determines the ratio relationships between color channels (R, G, B) and their corresponding grayscale values, storing these ratios for direct application during digitization. This eliminates the need for complex real-time calculations during image processing, thereby maintaining high color image quality while avoiding excessive system complexity.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the optimizing ratios based on the statistical parameters (mean μ and standard deviation σ) of each color channel's grayscale distribution. The system calculates these parameters from the captured image data and uses them to determine the appropriate ratio adjustments needed to compensate for channel sensitivity differences. This parameter-based approach allows the system to adapt to different lighting conditions and camera characteristics without requiring complex hardware modifications.
2Measurement precision
If optimizing ratios are calculated for each color channel based on Gaussian distribution, then color distortion is corrected effectively, but the calculation and processing time increases
Solution Approach 1:
The system performs preliminary calculation of the optimizing ratios using the Gaussian distribution parameters (mean and standard deviation) of each color channel. These ratios are calculated once based on the statistical characteristics of the color channels and can be stored for reuse. During actual image processing, the pre-calculated ratios are applied directly without requiring repeated complex calculations, thus achieving accurate color correction while minimizing processing time.
Solution Approach 2:
The patent replaces complex iterative optimization algorithms with a direct mathematical calculation based on Gaussian distribution parameters. Instead of using time-consuming mechanical or trial-and-error methods to determine optimal color channel ratios, the system uses closed-form mathematical expressions involving the mean (μ) and standard deviation (σ) of each channel's grayscale values. This substitution of mathematical modeling for iterative computation significantly reduces processing time while maintaining high color accuracy.
Data Source
AI summary
A color image processing system includes an image capture module, an image digitizing module, an optimizing ratio calculation module and an image processing module. The image capture module captures an original color image. The image digitizing module digitizes the original color image to obtain N pixels in ascending order according to value of RGB of the pixel. The optimizing ratio calculation module calculates an optimizing ratio H. The image processing module multiplies original value of RGB of the pixel before the ((N−N*H)/2)th pixel by 1/H to obtain new value of RGB of the pixel before the ((N−N*H)/2)th pixel and multiplies original value of RGB of the pixel after the ((N+N*H)/2−1)th pixel by H to obtain new value of RGB of the pixel after the ((N+N*H)/2−1)th pixel. A color image processing method is also provided.