Dynamic Color Matrix Selection for Image Sensor Noise
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Solution Overview
Problem
Image sensors in cameras capture varying raw color values for the same object due to different filter spectra, leading to inconsistencies in color representation, which existing color conversion methods struggle to accurately address, especially under different noise conditions and camera settings.
Innovation Solution
An electronic device with an image sensor and a processor that selects and applies specific color conversion matrices based on noise information, exposure time, f-number, ISO value, and white balance gain to generate a color-converted image, minimizing color difference and noise amplification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single color conversion matrix is used for all images, then the device complexity is reduced, but the color accuracy deteriorates under different noise conditions and camera settings
Solution Approach 1:
The system dynamically selects the appropriate color conversion matrix based on noise information and camera settings rather than using a static single matrix. This allows the color conversion process to adapt to different imaging conditions, thereby maintaining high color accuracy without requiring an overly complex multi-matrix system for every possible scenario.
Solution Approach 2:
The system changes the parameter of color conversion matrix selection based on noise information and camera settings. By adjusting which matrix is applied according to the specific imaging conditions, the system achieves accurate color conversion across varying conditions without permanently increasing device complexity.
2Measurement precision
If multiple color conversion matrices are stored and selected based on noise information, then the color accuracy is improved, but the device complexity increases
Solution Approach 1:
The system applies different color conversion matrices to different local conditions (noise levels and camera settings) rather than using a universal matrix. This local quality approach ensures that each imaging scenario receives the most appropriate color conversion treatment, improving accuracy without requiring a complete set of matrices for all conceivable scenarios.
Solution Approach 2:
The system manages multiple matrices by changing the selection parameter based on noise information and camera settings. This parameter-driven selection strategy allows the system to handle multiple matrices efficiently, improving color accuracy for specific conditions without proportionally increasing overall device complexity.
3Measurement precision
If a color conversion matrix minimizing color difference is selected, then the color accuracy is improved, but the noise amplification increases
Solution Approach 1:
The system changes the selection criterion parameter from solely minimizing color difference to a composite consideration that includes both color accuracy and noise characteristics. By evaluating matrices based on multiple parameters (color difference and noise amplification), the system selects the most appropriate matrix for each specific imaging condition, balancing both concerns rather than optimizing for one at the expense of the other.
Solution Approach 2:
The system applies different color conversion matrices to different local noise conditions. For low-noise scenarios, matrices optimized for color accuracy are selected, while for high-noise scenarios, matrices that balance color accuracy with noise control are chosen. This local quality approach ensures optimal performance for each specific noise level without universally sacrificing one quality for the other.
4Measurement precision
If noise information is obtained and processed to select color conversion matrices, then the color accuracy under different conditions is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary analysis of noise information and camera settings before the actual color conversion process. By preparing and evaluating matrix candidates in advance based on the imaging conditions, the system reduces the processing time required during the actual conversion, as the selection decision is already made or pre-determined based on the noise characteristics.
Solution Approach 2:
The system changes the processing approach by focusing noise analysis on key parameters (noise level, exposure time, f-number, ISO) rather than performing exhaustive analysis. This parameter-driven approach allows for efficient matrix selection based on dominant factors, improving color accuracy under different conditions without proportionally increasing processing time.
Data Source
AI summary
An electronic device includes an image sensor including a color filter having a plurality of color channels, a memory storing a plurality of color conversion matrices and instructions, and a processor. The processor is configured to obtain noise information of a color image captured by the image sensor, select a target matrix from among the plurality of the color conversion matrices based on the obtained noise information, and generate a color converted image by applying the selected target matrix to the color image.