Spectral Characteristics Prediction Using Relational Equations

Solution Overview

Problem

Current methods for predicting spectral characteristics in digital printing, such as the Deshpande et al. method, require printing and colorimetry of CxF charts, leading to increased costs and man-hours, especially when dealing with spot colors and intermediate gradation values, and often result in inaccuracies in color reproduction.

Innovation Solution

A spectral characteristics prediction method that sets a prediction target color with known maximum, minimum, and intermediate gradation values, calculates relational equations for sample colors, selects a reference color based on minimal difference between predicted and actual values, and uses these equations to accurately predict spectral characteristics at various gradation values.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If CxF charts are printed and colorimetry is performed to predict spectral characteristics, then prediction accuracy is improved, but costs and man-hours increase

Engineering Contradiction:

Improveprediction accuracyVSAvoidman-hours

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary measurement of spectral characteristics at maximum, minimum, and intermediate gradation values to establish relational equations in advance. These equations are then used to predict spectral characteristics at other gradation values without requiring additional CxF chart printing and colorimetry, thereby reducing time and cost while maintaining prediction accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates mathematical models (relational equations) that copy the relationship between different gradation values based on limited measurements. These equations serve as virtual copies that can predict spectral characteristics without physical measurement, eliminating the need to print and measure multiple CxF charts

Inventive Principle:

Principle #26Copying

2Measurement precision

If CxF charts are printed and colorimetry is performed to predict spectral characteristics, then prediction accuracy is improved, but costs increase

Engineering Contradiction:

Improveprediction accuracyVSAvoidcosts

Core Design Contradiction:

Measurement precisionVSLoss of substance

Solution Approach 1:

The system performs preliminary measurement of spectral characteristics at maximum, minimum, and intermediate gradation values to establish relational equations in advance. These equations are then used to predict spectral characteristics at other gradation values without requiring additional CxF chart printing and colorimetry, thereby reducing time and cost while maintaining prediction accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates mathematical models (relational equations) that copy the relationship between different gradation values based on limited measurements. These equations serve as virtual copies that can predict spectral characteristics without physical measurement, eliminating the need to print and measure multiple CxF charts

Inventive Principle:

Principle #26Copying

3Manufacturing precision

If spectral characteristics at multiple gradation values are measured to improve prediction accuracy, then color reproduction accuracy is improved, but the complexity of the process increases

Engineering Contradiction:

Improvecolor reproduction accuracyVSAvoidprocess complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system changes the approach from measuring multiple parameters (spectral characteristics at many gradation values) to measuring fewer key parameters (maximum, minimum, and intermediate gradation values) and using mathematical relationships to derive the remaining parameters. This reduces process complexity while maintaining color reproduction accuracy

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system introduces relational equations as intermediary mathematical models that connect the measured spectral characteristics at key gradation values to the predicted spectral characteristics at other gradation values. These equations serve as mediators that simplify the complex relationship between different gradation values

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentEP4087226B1Spectral characteristics prediction method and spectral characteristics prediction program

Publication Date: 2024.08.07 SCREEN HOLDINGS CO LTD

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AI summary

First relational equations which represent characteristics of respective sample colors are obtained (S110), and for the respective sample colors, prediction values of spectral characteristics of characteristics-acquired gradation values for a prediction target color are obtained using the first relational equations (S120). Difference values between the prediction values and actual measurement values are obtained (S130), and a sample color for which a minimum difference value is obtained is selected as a reference color (S140). A second relational equation that represents characteristics of the reference color is obtained (S150), and a prediction value of spectral characteristics of a prediction target gradation value for the prediction target color is obtained using the second relational equation (S160).