Neural Network Color Prediction for Overprint Accuracy
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Solution Overview
Problem
Conventional methods for predicting colors obtained by overprinting process color inks and spot color inks lack accuracy, particularly when using spectral reflectance data, leading to increased costs and inefficiencies in color reproduction.
Innovation Solution
A color prediction method employing machine learning with a neural network to correct spectral characteristics of process color inks, ensuring the difference between tristimulus values is minimized, thereby improving the accuracy of overprint predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional methods (Deshpande et al. method) are used to predict overprint colors, then the prediction process is relatively simple and easy to implement, but the prediction accuracy is insufficient
Solution Approach 1:
The patent transforms the input color data from simple tristimulus values (XYZ) to spectral reflectance data through a neural network. This parameter transformation enables more accurate overprint predictions by capturing wavelength-specific reflectance characteristics rather than relying on integrated color values, thereby resolving the contradiction between implementation simplicity and prediction accuracy.
Solution Approach 2:
The patent replaces the conventional linear algebra-based prediction mechanism (Deshpande et al. method using Equations 1-3) with a neural network-based spectral transformation mechanism. This substitution allows the system to learn complex nonlinear relationships between process color inks and spectral characteristics, significantly improving prediction accuracy while maintaining computational feasibility through the neural network's efficient architecture.
2Measurement precision
If spectral reflectance data is used for color prediction, then prediction accuracy improves, but the complexity of the prediction process increases
Solution Approach 1:
The patent introduces a neural network as an intermediary component that bridges the gap between simple tristimulus input data and complex spectral reflectance requirements. The neural network acts as a spectral transformation engine that generates wavelength-specific reflectance values without requiring direct spectral measurement, thereby improving accuracy while managing process complexity through a dedicated computational module.
Solution Approach 2:
The patent performs preliminary spectral transformation of process color ink data into spectral reflectance form using the neural network before conducting the overprint prediction calculation. This preliminary action prepares the data in the appropriate format for accurate spectral-based overprint computation, separating the complex spectral generation step from the prediction calculation step and thereby managing overall process complexity.
3Measurement precision
If a neural network is used to transform tristimulus values to spectral reflectance, then color prediction accuracy for overprints is enhanced, but computational requirements increase
Solution Approach 1:
The neural network for spectral transformation is trained offline in advance using teaching data consisting of process color ink definitions and corresponding spectral reflectance measurements. Once trained, the network's weights and biases are fixed, allowing rapid online inference during actual overprint predictions. This preliminary training action separates the computationally intensive learning phase from the efficient prediction phase, enhancing accuracy while managing computational requirements during deployment.
Data Source
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AI summary
A color prediction method includes: a learning step (S110) of performing machine learning by a neural network that takes tristimulus values as input data and takes spectral reflectances as output data; an inference step (S130) of obtaining spectral reflectances for a processing-target process color ink by the learned neural network; a correction step (S140) of correcting the spectral reflectances; and a prediction step (S150) of predicting spectral reflectances in a state where a processing-target spot color ink is applied on the processing-target process color ink. In the correction step (S140), the spectral reflectances after the correction is obtained such that the difference between the tristimulus values of a prediction source and tristimulus values corresponding to the spectral reflectances after the correction is small, while an increase in a difference between the spectral reflectances before the correction and the spectral reflectances after the correction is suppressed.