Color Prediction Method Using Spectral Relational Expressions
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Solution Overview
Problem
Current methods for predicting colors in digital printing, such as the Deshpande et al. method, require printing and colorimetry of CxF charts on multiple media, leading to increased costs and man-hours, especially when changing print media, and often necessitate a large amount of teaching data for accurate color prediction.
Innovation Solution
A color prediction method that selects a similar color close to the target color and calculates a relational expression between the spectral characteristics of a reference medium and a prediction target medium, allowing for accurate prediction of spectral characteristics of patches without extensive data requirements, thereby reducing processing load and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Deshpande et al. method is used to predict colors on multiple media, then color prediction accuracy is improved, but the cost and man-hours increase significantly
Solution Approach 1:
The patent creates a virtual color chart through calculation rather than physically printing and measuring multiple color charts on different media. The relational expression allows prediction of spectral characteristics without actual printing, effectively copying the measurement process through mathematical computation, thereby eliminating the need for physical prototypes and reducing material costs.
Solution Approach 2:
The patent replaces the mechanical process of printing physical color charts and performing physical colorimetry with a computational system. By using relational expressions and spectral characteristic calculations, the mechanical measurement process is substituted with mathematical computation, significantly reducing manual labor and time requirements.
2Manufacturing precision
If CxF charts are printed and colorimetry is performed on multiple media to obtain accurate prediction values, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The patent pre-calculates and stores spectral characteristics of inks and media in a database, and establishes relational expressions in advance. When color prediction is needed, the system directly retrieves and computes using pre-prepared data, eliminating the need for time-consuming physical measurements each time a prediction is required, thus significantly improving processing speed.
Solution Approach 2:
The patent replaces the slow mechanical process of physical printing and colorimetry with rapid computational methods. By using spectral characteristic calculations and relational expressions, the system achieves instant color predictions without waiting for physical measurement processes, dramatically increasing productivity.
3Measurement precision
If a large amount of teaching data is collected for accurate color prediction, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential spectral characteristics of inks and media that are needed for color prediction, storing them in a database. Instead of processing large amounts of redundant teaching data, the system uses only the critical spectral information and relational expressions, simplifying the data structure and reducing processing complexity while maintaining prediction accuracy.
4Adaptability or versatility
If colorimetry is performed on multiple media to account for media differences, then adaptability is improved, but loss of time increases
Solution Approach 1:
The patent incorporates media-specific parameters (spectral characteristics of each medium) into the relational expression calculations. By changing the media parameter in the calculation, the system can predict colors on any medium without physical re-measurement, maintaining adaptability to different media while eliminating the time required for actual printing and measurement on each medium.
Data Source
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AI summary
First, a color close to a prediction target color is selected as a first similar color from among a plurality of similar candidate colors (S110). Next, a first relational expression representing a relationship of spectral reflectances of a solid patch between a reference medium and a prediction target medium is obtained for the first similar color (S120). Then, spectral reflectances of the solid patch in the prediction target medium for the prediction target color are predicted by applying spectral reflectances of a solid patch in the reference medium for the prediction target color to the first relational expression (S130). Finally, spectral reflectances of each halftone patch in the prediction target medium for the prediction target color are predicted based on the prediction result (S140).