Image Color Conversion Using Pixel-Wise Gamut Matrices
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Solution Overview
Problem
Existing image processing devices fail to accurately adjust color tones across different color gamuts, resulting in inconsistent color representation due to the application of a single color conversion matrix to the entire image.
Innovation Solution
An image processing device that applies different color conversion matrices based on the color gamut of individual pixels, utilizing a color conversion module and processor to calculate and adjust color gamuts using hue values, segmentation information, and saturation values, enabling personalized color tone expression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single color conversion matrix is applied to the entire image, then the processing complexity is reduced, but the color accuracy and tone consistency across different color gamuts deteriorates
Solution Approach 1:
The image is segmented into multiple regions based on color gamut characteristics, with each region assigned an appropriate color conversion matrix. This segmentation approach allows different processing strategies for different color regions, improving overall color accuracy without uniformly increasing complexity across the entire image processing system.
Solution Approach 2:
Different color conversion matrices are applied to different local regions of the image based on their specific color gamut characteristics. This local quality principle ensures that each region receives the most appropriate color conversion treatment, optimizing color accuracy for diverse color content while maintaining manageable processing complexity through selective application.
2Measurement precision
If different color conversion matrices are applied based on color gamut, then the color accuracy is improved, but the processing complexity increases
Solution Approach 1:
The system dynamically selects and applies different color conversion matrices based on the detected color gamut of each image region. This dynamic adaptation allows the processing complexity to vary locally rather than being uniformly high across the entire system, achieving high color accuracy where needed while maintaining simpler processing in other areas.
Solution Approach 2:
The color conversion parameters (matrices) are changed according to the color gamut characteristics of different image regions. This parameter adaptation enables precise color control for each region's specific color properties, improving overall color accuracy while the systematic approach to parameter selection helps manage processing complexity.
3Speed
If color conversion is performed without considering hue values and saturation, then the processing speed is maintained, but the color tone expression fidelity deteriorates
Solution Approach 1:
The system performs preliminary analysis of hue values and saturation characteristics before applying color conversion matrices. This preliminary action allows the system to prepare and select the appropriate conversion parameters in advance, minimizing actual processing time while ensuring that color tone fidelity is preserved through informed conversion decisions.
Solution Approach 2:
The color conversion process utilizes the image's own hue and saturation information to determine the appropriate conversion parameters. This self-service approach means the image data itself guides the conversion process, eliminating the need for external parameter input and maintaining processing efficiency while preserving color tone authenticity.
Data Source
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AI summary
An image processing device includes a memory device that stores a color conversion module, and one or more processors that access the memory device and execute the color conversion module to calculate a color gamut of an input image and perform color conversion of the input image by applying different color conversion matrices according to the color gamut.