White Detection Using Luminance and Window Division
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Solution Overview
Problem
Existing white detecting methods in imaging apparatuses face errors in color correction due to high luminance of monochrome data being mixed with color data and frequent changes in color sense caused by selecting adjacent windows, leading to instability and increased error probability in white balancing.
Innovation Solution
A method and apparatus that decide whether color data exists within a predetermined white area by measuring distances and subdividing the area into sub-white areas, detecting color data closest to the white trace with higher luminance, and calculating mean values from divided windows to accurately identify white for stable color correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If luminance division method is used to detect white, then white detection can be performed, but high luminance of monochrome data is mixed with color data causing increased error probability
Solution Approach 1:
The white area is divided into multiple sub-white areas (first, second, third sub-white areas) based on different luminance ranges. By segmenting the detection space and selecting color data from specific sub-areas, the method prevents mixing of monochrome high-luminance data with color data, thereby improving both detection accuracy and correction reliability
Solution Approach 2:
Different sub-white areas are assigned different luminance range characteristics. The first sub-white area corresponds to a specific luminance range that excludes high-luminance monochrome regions. By applying local quality differentiation across sub-areas, the method ensures color data purity while maintaining detection precision
2Adaptability or versatility
If window division method is used to detect white, then white balancing can be performed, but the selected window is frequently changed causing minute changes in color sense
Solution Approach 1:
The detection area is segmented into multiple sub-white areas with defined luminance ranges rather than using discrete windows. This segmentation provides a continuous, stable reference framework that prevents frequent changes in color sense while maintaining adaptability for white balancing across different lighting conditions
Solution Approach 2:
The method uses luminance range parameters to define sub-white areas instead of fixed window positions. By changing from spatial window selection to luminance-based area definition, the system achieves stable color sense while adapting to varying light conditions through parameter adjustment rather than window repositioning
3Adaptability or versatility
If color data from adjacent luminance ranges is used, then white detection flexibility is improved, but white continuously varies resulting in small changes in color sense
Solution Approach 1:
The luminance range is segmented into distinct sub-white areas with non-overlapping or minimally overlapping boundaries. Each sub-area corresponds to a specific luminance range, preventing continuous variation of white detection results while maintaining flexibility through selection of appropriate sub-areas based on imaging conditions
Solution Approach 2:
Each sub-white area is assigned a specific luminance range characteristic that provides localized color data quality. By ensuring that color data comes from sub-areas with appropriate luminance characteristics rather than adjacent ranges, the method maintains both detection flexibility and color sense consistency
Data Source
AI summary
A white detecting method and apparatus using the same are disclosed, wherein the white detecting method includes steps for using a luminance division and/or using a window division for white detecting. The luminance division detects a white by using at least one color data information existing in a white area, and the window division detects a mean value of at least one color data information existing in the white area as a white. With this, the white which forms a reference in color correction, can be accurately detected, thereby improving a stability of color sense and reducing an error probability in white balancing.