Adaptive White Balance Algorithm Selection for Image Processing
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Solution Overview
Problem
Existing image processing technologies lack an adaptive white balance algorithm that effectively adjusts to varying lighting environments, often resulting in mismatched white balance settings when selected by users, leading to suboptimal image processing outcomes.
Innovation Solution
A method and device that calculate a first gain using the Face Automatic White Balance (FaceAWB) algorithm and a second gain using the simple gray world algorithm, determining their similarity to perform white balance processing accordingly, ensuring the algorithm matches the imaging scenario and improves white balance effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user selection method is used for white balance algorithm, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system automatically detects the imaging scenario and selects the appropriate white balance algorithm without user intervention. The processor autonomously determines whether to use FaceAWB or gray world algorithm based on scene analysis, eliminating the need for manual user selection while maintaining high accuracy.
Solution Approach 2:
The system changes the algorithm selection parameter dynamically based on detected imaging conditions. By analyzing scene characteristics and adjusting the white balance algorithm choice according to environmental parameters, the system achieves both automated operation and high processing precision.
2Device complexity
If single white balance algorithm is used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The imaging device incorporates multiple white balance algorithms (FaceAWB and gray world algorithm) to handle different imaging scenarios. Each algorithm serves specific lighting conditions, making the system universally applicable across diverse environments without requiring complex manual configuration.
Solution Approach 2:
The system dynamically selects between different white balance algorithms based on real-time scene analysis. The processor adjusts the algorithm choice according to lighting conditions, transforming the static single-algorithm approach into a dynamic multi-algorithm system that adapts to varying environments.
3Manufacturing precision
If FaceAWB algorithm is always used, then manufacturing precision for face images is improved, but adaptability to other scenarios deteriorates
Solution Approach 1:
The system applies different white balance algorithms to different imaging scenarios. FaceAWB is specifically used for face-containing images where skin tone accuracy is critical, while gray world algorithm is used for other scenarios. This localized algorithm selection optimizes precision for each specific case without compromising overall system versatility.
Data Source
AI summary
A method and device for processing white balance of an image, and storage medium are provided. The method includes that: a first gain of an image is calculated according to a Face Automatic White Balance (FaceAWB) algorithm configured to regulate a face in the image to a skin color; a second gain for the image is calculated according to a simple gray world algorithm; whether the first gain is similar to the second gain is determined; and responsive to a determination that the first gain is similar to the second gain, white balance processing is performed on the image according to the second gain, and responsive to a determination that the first gain is not similar to the second gain, white balance processing is performed on the image according to the first gain.