Sensor-Independent Color Conversion for Cross-Sensor White Balance
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Solution Overview
Problem
Existing automatic white balancing (AWB) algorithms face challenges in accurately estimating the dominant illumination color from raw images captured by different camera sensors, especially when training datasets are limited and diverse, leading to impractical solutions for exotic color filter array types and high computational costs.
Innovation Solution
The method involves generating a sensor-independent representation of images using a sensor-specific color conversion function optimized in a chromaticity space, allowing for the application of single-sensor color constancy algorithms across various sensor types without retraining, and performing white balancing by converting between sensor-independent and sensor-specific illuminance estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different algorithms and methods are developed for each sensor type to achieve accurate white balancing, then white balancing accuracy is improved, but device complexity and development cost increase
Solution Approach 1:
The patent develops a universal white balancing algorithm that can process images from multiple sensor types (RGB, CFA, exotic color filter arrays) using a single unified approach. The system creates sensor-independent representations that work across different sensor configurations, eliminating the need for separate algorithms for each sensor type while maintaining accurate color correction.
Solution Approach 2:
The patent transforms the white balancing problem by changing the parameter space from sensor-specific RGB values to sensor-independent chromaticity coordinates. This parameter transformation allows the same algorithm to handle diverse sensor types by operating in a standardized color space that abstracts away sensor-specific characteristics.
2Measurement precision
If extensive training data is collected for each sensor type to improve white balancing performance, then algorithm accuracy is improved, but loss of time and computational resources increase
Solution Approach 1:
The patent creates sensor-independent representations that copy and transform color information into a universal format. Instead of training separate models for each sensor, the system uses a single training process that learns to operate in the sensor-independent space, effectively copying the solution across all sensor types without retraining.
3Measurement precision
If sensor-specific solutions are developed for exotic color filter array types to achieve accurate white balancing, then white balancing accuracy is improved, but ease of manufacture and deployment worsen
Solution Approach 1:
The patent creates a universal white balancing solution that handles exotic color filter arrays and standard sensors through a single algorithmic framework. The system processes diverse sensor inputs by converting them to sensor-independent representations, making implementation straightforward without requiring separate development paths for different sensor types.
Data Source
AI summary
Learning-based color correction (e.g., auto while balance (AWB)) procedures may be trained based on datasets from different sensors using a pre-processing procedure. Each input pixel may be converted into a sensor-independent representation through multiplication by a sensor-specific color conversion function (e.g., a 3×3 matrix). The sensor-specific color conversion function may be obtained based on a sensor type. For example, the sensor-specific color conversion function, such as a 3×3 matrix, may be obtained by a corresponding sensor calibration procedure performed using laboratory images of a color checker chart subject to standard illuminants. Parameters of the sensor-specific color conversion function may be optimized in a chromaticity space. For instance, a sensor-specific 3×3 matrix for color conversion may be optimized using a distance in the chromaticity space between calibration data (e.g., calibration configurations) and sensor-independent targets (e.g., a target sensor-independent representation for each calibration configuration).