Temporal White Balancer for Illumination Chromaticity Estimation
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Solution Overview
Problem
Existing automatic white balancing (AWB) techniques face challenges in accurately estimating illumination chromaticity, especially in difficult scenes with large monochromatic objects and varying illumination conditions, leading to significant white balancing errors due to the ill-posed nature of the color constancy problem and reliance on camera module characterization.
Innovation Solution
The approach estimates illumination chromaticity in the temporal domain from a sequence of frames, leveraging spatio-temporal information and incorporating a scene illumination prior to stabilize white point estimation, combining with traditional statistical-based approaches to improve reliability and reduce errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional single-frame AWB algorithms are used, then the algorithm is simple and fast, but the white point estimation accuracy deteriorates in difficult scenes with large monochromatic objects
Solution Approach 1:
The patent transitions from single-frame analysis to temporal domain analysis by incorporating multiple consecutive frames. The white balancer processes a sequence of frames and uses temporal information to stabilize chromaticity estimation, effectively adding the time dimension to the problem. This allows the system to maintain algorithmic efficiency while significantly improving accuracy in difficult monochromatic scenes where single-frame methods fail.
Solution Approach 2:
The patent implements continuous white point estimation across multiple frames rather than independent single-frame processing. By continuously tracking and stabilizing chromaticity values across the frame sequence, the system maintains consistent color reproduction throughout the video, preventing the large estimation errors that occur with discrete single-frame analysis in challenging scenes.
2Device complexity
If camera module characterization is relied upon, then the AWB process is simplified, but the reliability deteriorates due to mass-production errors and varying illumination conditions
Solution Approach 1:
The patent implements a self-correcting AWB system that does not depend on pre-characterized camera module parameters. Instead, the white balancer automatically adapts to actual illumination conditions by analyzing the temporal sequence of frames and computing chromaticity values directly from the image data. This self-service approach eliminates reliance on mass-production characterization data, thereby improving reliability without significantly increasing system complexity.
Solution Approach 2:
The system dynamically adjusts white balancing parameters based on temporal analysis of multiple frames rather than using fixed characterization parameters. By computing chromaticity values that adapt to varying illumination conditions across the frame sequence, the system maintains reliable color reproduction under different lighting scenarios without depending on predetermined camera module characteristics.
3Use of energy by moving object
If single-frame AWB is used, then processing is computationally efficient, but color constancy deteriorates under varying illumination conditions
Solution Approach 1:
The patent extends the analysis from spatial domain (single frame) to temporal domain (sequence of frames). By incorporating the time dimension and analyzing chromaticity variations across multiple frames, the system achieves robust color constancy under varying illumination conditions. The temporal averaging and stabilization processes improve measurement precision while maintaining computational efficiency through optimized processing of the frame sequence.
Data Source
AI summary
A method, system, and apparatus are described. The method includes calculating, via a white balancer, a candidate illumination chromaticity estimate for a current frame and calculating, via a scene prior monitor, a scene prior chromaticity for the current frame as a weighted sum of a scene prior chromaticity from a previous frame and a candidate illumination chromaticity estimate. The method also includes calculating, via a scene invariant illumination chromaticity controller, a final illumination chromaticity for the current frame as a weighted sum of a scene illumination chromaticity for the current frame and the candidate illumination chromaticity estimate.