Color Processing Apparatus Using Segmented Measurement for Accuracy
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Solution Overview
Problem
Current color processing systems face inefficiencies in color measurement due to the time and effort required for scanning and measuring images, particularly when dealing with specific colors that lack associations between CMYK and RGB spaces, leading to inaccurate color conversions and increased operational complexity.
Innovation Solution
A color processing apparatus that generates pattern images with specific colors arranged in a grid and line patterns, using a combination of image reading and color measurement units to acquire data, convert colors between spaces, and generate transformation equations for accurate color representation, thereby optimizing the color measurement process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If color measurement is performed on all measurement images, then color conversion accuracy is improved, but measurement time and operational complexity increase
Solution Approach 1:
The measurement images are divided into two groups: specific-color measurement images (containing colors without CMYK-RGB associations) and other measurement images. The system selectively processes only the specific-color images through both reading and measurement, while other images are processed through reading only. This segmentation reduces measurement time while maintaining color conversion accuracy for critical colors.
Solution Approach 2:
Different processing qualities are applied to different types of measurement images. Specific-color images receive full processing (reading + measurement) to ensure high accuracy for colors lacking standard associations, while other images receive standard processing (reading only). This local quality approach optimizes resource allocation based on the specific needs of each image type.
2Measurement precision
If color measurement is performed on all measurement images, then color conversion accuracy is improved, but operational complexity increases
Solution Approach 1:
The operational process is segmented into different processing paths based on image type. The system automatically identifies specific-color measurement images and routes them through the full processing chain, while routing other images through a simplified path. This segmentation reduces operational complexity by providing clear, differentiated procedures.
Solution Approach 2:
The system performs preliminary identification of specific-color measurement images before processing. By pre-categorizing images based on their color characteristics and CMYK-RGB association status, the system establishes the processing path in advance, reducing operational complexity during the actual measurement and conversion processes.
3Productivity
If specific colors are arranged in a line pattern, then measurement efficiency is improved, but the representation of color diversity decreases
Solution Approach 1:
The system uses grid patterns for measurement images where multiple colors are arranged in two-dimensional space, allowing efficient measurement of diverse colors simultaneously. The line pattern is used specifically for specific-color images to optimize measurement efficiency. This dimensional approach allows the system to maintain color diversity representation while achieving measurement efficiency through appropriate pattern selection.
Data Source
AI summary
A color processing apparatus includes a processor configured to implement first, second, and third acquiring units. The first acquiring unit acquires first color data indicating association among first-space color in a first space, second-space color obtained by reading measurement images having different first-space colors, and third-space color obtained by converting the second-space color through a predetermined transformation model. The second acquiring unit acquires second color data indicating association between the first-space color and the third-space color obtained by performing color measurement on some of the specific-color measurement images containing a specific color in the first space and included in the measurement images. The third acquiring unit acquires third color data indicating association between the first-space color and the third-space color obtained by converting the first-space color through a transformation equation obtained from the first and second color data. The conversion is performed on the other specific-color measurement images.