Color Conversion Table Creation via Polyhedron Space Segmentation
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Solution Overview
Problem
Existing methods for creating color conversion tables are inefficient due to excessive interpolation, which increases calculation time despite improving accuracy with more interpolation points.
Innovation Solution
A conversion table creating device and method that divides color spaces into polyhedron spaces with independently set division numbers, using interpolation points to calculate color data in one-to-one correspondence, thereby creating a conversion table efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of interpolation points is increased to improve color conversion accuracy, then the accuracy is improved, but the calculation time increases
Solution Approach 1:
The color space is divided into multiple polyhedron spaces, and each polyhedron space is further divided into smaller sub-polyhedron spaces. This segmentation allows interpolation to be performed locally within smaller regions rather than across the entire color space, reducing the number of interpolation points needed while maintaining accuracy.
Solution Approach 2:
Different division numbers are set for different polyhedron spaces based on their local characteristics. Regions requiring higher precision can have finer division, while other regions can use coarser division. This local adaptation optimizes the balance between accuracy and calculation efficiency.
2Measurement precision
If interpolation is performed excessively to ensure high accuracy, then the color conversion accuracy is improved, but the efficiency of creating the color conversion table deteriorates
Solution Approach 1:
By dividing the color space into polyhedron spaces and then into smaller sub-polyhedron spaces, the interpolation process is localized. This reduces the total number of interpolation points required compared to uniform interpolation across the entire color space, thereby improving creation efficiency while maintaining accuracy.
Solution Approach 2:
Instead of performing interpolation uniformly across the entire color space, the method applies interpolation selectively within smaller polyhedron spaces. This partial action approach avoids excessive interpolation in regions where it is not needed, optimizing the balance between accuracy and efficiency.
Data Source
AI summary
Each of a plurality of polyhedron spaces is defined by a plurality of first vertices. Each of the plurality of first vertices is defined by a plurality of sets of third color data that are defined in the second color space. A color data setting unit sets a plurality of sets of fourth color data that are defined in a first color space in one to one correspondence with the plurality of third color data sets. A second dividing unit divides each of a plurality of polyhedron spaces into a plurality of smaller polyhedron spaces by a division number set for the each polyhedron spaces. Each of the plurality of smaller polyhedron spaces is defined by a plurality of second vertices and the plurality of first vertices. Each of the plurality of second vertices is defined by a plurality of sets of fifth color data defined in the second color space. An interpolation calculation unit calculates a plurality of sets of sixth color data that are defined in the first color space in one to one correspondence with the plurality of second vertices by interpolating the plurality of the fourth color data sets. A conversion table creating unit creates a conversion table based on the fourth color data sets and the sixth color data sets and on the third color data sets and the fifth color data sets.