Color Data Transformation Using TRV Correction Tables
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Solution Overview
Problem
Existing color transformation methods struggle with high-dimensional color spaces, leading to large memory requirements and inaccurate results when transforming color data from a source color space to a target color space, especially in printing processes that use more than four colors, due to the exponential increase in table size and the inability to account for color interactions in overprints.
Innovation Solution
A method involving a transformation rule TRV combined with a correction process using multidimensional tables with few interpolation points, applying an optimization procedure to minimize differences between nominal and actual values, and employing a sequence of correction tables to adjust input and output values, ensuring accurate color transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color transformation methods are used with high-dimensional color spaces, then color transformation accuracy is improved, but memory requirements increase exponentially
Solution Approach 1:
The patent divides the high-dimensional color transformation problem into multiple lower-dimensional transformation steps. Instead of creating a single large transformation table for all colors, the system performs transformations in stages, handling one or a few colors at a time through sequential transformation rules, thereby reducing memory requirements while maintaining accuracy.
Solution Approach 2:
The patent transforms the problem from a high-dimensional color space directly to a low-dimensional representation. By using transformation rules that map colors through intermediate color spaces and applying corrections based on measured values, the system reduces the dimensionality of the transformation tables needed, reducing memory usage while preserving transformation accuracy.
2Measurement precision
If traditional color transformation methods are used with high-dimensional color spaces, then color transformation accuracy is improved, but the complexity of the transformation system increases
Solution Approach 1:
The patent segments the complex color transformation into multiple simpler transformation rules applied in sequence. Each rule handles a specific color or group of colors, making the overall system more manageable and less complex while achieving high accuracy through the cumulative effect of these simplified rules.
Solution Approach 2:
The patent introduces intermediate transformation stages and correction mechanisms that act as mediators between the source color space and target color space. These intermediaries simplify the direct transformation by breaking it down into manageable steps with correction tables that adjust for specific color interactions, reducing overall system complexity.
3Productivity
If traditional color transformation methods are used, then color transformations can be performed, but they fail to accurately account for color interactions in overprints
Solution Approach 1:
The patent implements feedback mechanisms through correction tables that are generated by comparing transformation results with actual measured values. The differences between predicted and measured color values are used to adjust and refine the transformation rules, enabling the system to accurately account for color interactions in overprints through iterative correction.
Solution Approach 2:
The patent changes the parameters of the transformation system by introducing correction factors and adjustment values that are specific to color interactions. These parameter changes allow the system to adapt to the specific behavior of color combinations in overprints, improving accuracy without requiring a complete redesign of the transformation approach.
Data Source
AI summary
The invention includes a method for performing transformations of color data, where the results of a transformation rule TRV can be corrected to a verified nominal value data set of the m values of the target color space Z1, which is determined for a set of color data Q1, from the n colors of the source color space. A copy of Q1 is created as data set Q2. The set of color data Q2 is transformed using the provided transformation rule TRV and creates another data set of the m values of the target color space Z2. The data set Q2 is optimized and transformed back to Z2 until the differences between Z1 and Z2 are minimized. The difference between the original data set Q1 and the optimized data set Q2 and the optimized result Z2 and the target data set Z1 can be applied to the transformations of color data.