White Balance Analysis Using Window Continuity and Light Source Weighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional white balance methods neglect the overall picture content and image light source, leading to misjudgments and biased results, especially when analyzing scenes with large blue skies or varying light sources.
Innovation Solution
A white balance method that divides an image into multiple windows, analyzes the light source type, position, and continuity between windows to determine accurate color white identification results, using weights to calculate a white balance estimated value and adjust the white balance process accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the image is divided into multiple windows and color white identification is performed on each window, then the white balance analysis becomes more detailed, but the complexity of the device increases
Solution Approach 1:
The image is divided into multiple windows, and color white identification is performed on each window separately. This segmentation allows detailed analysis of different regions while managing complexity through systematic processing of individual segments.
Solution Approach 2:
The color white identification results from multiple windows are combined through weighted averaging to produce a comprehensive white balance estimate. This merging integrates information from all regions while maintaining manageable complexity through a unified calculation approach.
2Reliability
If stricter limitation is imposed on image data positions and white pixel detection range, then high color temperature pixels are excluded from analysis, but the reliability of white balance correction decreases
Solution Approach 1:
Different windows are assigned different weights based on their position and content characteristics. This local quality approach allows certain regions to have more influence on the white balance calculation while others have less influence, optimizing the use of available image data.
Solution Approach 2:
The system evaluates the continuity of color temperature across surrounding windows and adjusts the weight of each window accordingly. This feedback mechanism ensures that windows with consistent color temperature contribute more to the white balance estimate, improving reliability while utilizing available information.
3Measurement precision
If the weight of color white identification result is determined based on window position and continuity, then the white balance estimated value becomes more accurate, but the processing time increases
Solution Approach 1:
The continuity between surrounding windows is evaluated in advance, and weights are determined before the final white balance calculation. This preliminary action prepares the data structure for efficient computation, reducing processing time while maintaining accuracy.
Solution Approach 2:
The weight parameters are dynamically adjusted based on window position and color temperature continuity. This parameter change approach allows the system to adapt to different scene conditions while using efficient mathematical operations to maintain reasonable processing speed.
Data Source
AI summary
An entered image is divided into a plurality of windows and it is determined, on a per-window basis, whether the image data within an applicable window is indicative of the color white, based upon the position of each window in the image and the continuity to its surrounding divided windows. A white balance method is performed based upon data of a window determined to be indicative of the color white. The present invention conducts a white balance process through the addition of determining the light source type weight and calculating the continuity of each divided window and its surrounding divided windows. In order the white balance process to accurately obtain the colors of an entered image even under the conditions of specific scenes with distinctive colors.