Dynamic Region Adjustment for Color Conversion Model Precision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The precision of color conversion models prepared by machine learning using pre-color conversion and post-color conversion images is reduced when images are captured under poor conditions or subjected to processing like enlargement, reduction, or compression, due to impaired or incorrect color data.
Innovation Solution
An image processing apparatus that extracts color data from a region of interest, adjusts the position or range of this region if the data meets certain conditions, and prepares a color conversion model using the adjusted data to ensure precision, by evaluating the suitability of the color data based on variations and noise levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color data is extracted from a fixed region without adjustment, then the processing is simple and fast, but the precision of the color conversion model is reduced when images are captured under poor conditions or subjected to processing
Solution Approach 1:
The region for color data extraction is made dynamic rather than fixed. The processor adjusts the position and/or range of the extraction region based on evaluation results, allowing the system to adapt to different image conditions (poor capture conditions, enlargement, reduction, compression) and maintain high precision in color conversion model preparation
Solution Approach 2:
The system implements a feedback mechanism where the processor evaluates extracted color data and uses this evaluation to determine whether to adjust the extraction region. This closed-loop approach ensures that only suitable color data is used for model preparation, improving precision while maintaining efficiency through conditional adjustment
2Measurement precision
If all extracted color data is used for model preparation, then the processing is efficient, but the precision is reduced due to inclusion of impaired or incorrect color data
Solution Approach 1:
The system extracts and isolates suitable color data from the extracted data set based on evaluation criteria. By separating suitable data from unsuitable data (impaired or incorrect color data), the system ensures that only high-quality data is used for color conversion model preparation, maintaining both precision and efficiency
Solution Approach 2:
The system applies different quality standards to different regions of the image. By evaluating color data from specific regions and selectively using only suitable data, the system ensures local quality control, which contributes to overall model precision without requiring complete reprocessing of all image data
Data Source
AI summary
An image processing apparatus includes: an input device to which a pre-color conversion image and a post-color conversion image are input; and a processor. The processor is configured to execute a program to extract color data in a certain region of at least one of the pre-color conversion image and the post-color conversion image, change at least one of a position and a range of the certain region in a case where the extracted color data meet a certain condition, and prepare a color conversion model using color data in the certain region after being changed.