Image Processing Color Space Conversion
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Solution Overview
Problem
Existing image processing technologies face challenges in accurately converting image data between different color spaces, particularly when dealing with color gamuts that are not directly compatible, leading to issues like detail loss and hue changes during color space conversion.
Innovation Solution
A method and system that utilize multiple color space conversion processes, including color conversion matrices (CCMs) and Lookup-Tables (LUTs), to process image data from an input color space to an output color space, allowing for the selection and blending of processed data to optimize color representation based on the content gamut, thereby mitigating the limitations of single conversion methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single color space conversion process is used, then the conversion speed is fast, but color accuracy and preservation of details deteriorate
Solution Approach 1:
The color space conversion process is segmented into multiple parallel conversion paths, each using a different conversion function (e.g., first conversion function for certain color ranges, second conversion function for other ranges). This allows each segment to be optimized for specific color characteristics, improving overall color accuracy while maintaining manageable complexity through modular processing
Solution Approach 2:
Different conversion functions are applied to different portions of the color space or different color characteristics within the image data. The system selects and applies the most appropriate conversion function locally based on the specific color properties, ensuring optimal color representation for each region rather than using a single uniform conversion approach
2Measurement precision
If multiple color space conversion processes are used, then color accuracy and saturation are improved, but processing time and computational complexity increase
Solution Approach 1:
Multiple conversion results are merged through a blending process that combines the output of different conversion functions into a final processed image data set. The blending operation integrates the strengths of each conversion function while mitigating their individual weaknesses, achieving high color accuracy without requiring all conversions to be processed sequentially
Solution Approach 2:
The system applies multiple conversion functions (excessive action) to ensure comprehensive color coverage, then uses blending to retain only the essential information from each conversion. This partial retention approach ensures that the final output achieves the required color representation accuracy without processing every possible conversion path fully, thereby managing processing time
3Measurement precision
If color conversion functions are optimized for specific color gamuts, then color saturation is improved, but adaptability to different display standards deteriorates
Solution Approach 1:
The system employs multiple conversion functions that are each designed to handle specific color gamut characteristics, making the overall system universal. The blending mechanism acts as a mediator that ensures compatibility across different display standards by integrating the specialized outputs of each conversion function, allowing the system to adapt to various Rec. 2020, Rec. 709, and other color space requirements
Data Source
AI summary
A computer-implemented method, a computer system configured to perform the method, and a non-transitory computer-readable storage medium are provided comprising instructions for executing the method are provided. The computer-implemented method comprises obtaining input video data including frames of input image data comprising first image data values expressed in an input color space. The first image data values are processed with a first and second color space conversion process to generate first processed image data and second processed image data respectively. The first processed image data and the second processed image data include image data values expressed in an output color space. Output image data is derived from both the first processed image data and the second processed image data.