Color Data Transformation via Sub-Component Segmentation
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Solution Overview
Problem
Current color transformation technologies face challenges in accurately transforming color data from a source color space with n colors into a target color space with M values, especially when dealing with combinations of colors for which no transformation tables are available, leading to inefficiencies and inaccuracies in color reproduction.
Innovation Solution
A procedure that uses a transformation schedule to estimate the color effect of each color in a combination by breaking down the mixture into known sub-components, applying blending modes from computer graphics, and gradually constructing output values for unknown input combinations from known input combinations, allowing for high-accuracy transformations without relying on N-colored transformation tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformation tables are used for all color combinations, then transformation accuracy is improved, but memory requirements and processing complexity increase exponentially
Solution Approach 1:
The patent segments the color transformation process by dividing n-color combinations into smaller sub-combinations (k-color where k<n). Instead of creating one massive n-dimensional transformation table, the system creates multiple smaller tables for subsets of colors, significantly reducing memory requirements while maintaining accuracy through compositional combination of these sub-transformations.
Solution Approach 2:
The patent implements a nested structure where transformation tables for smaller color subsets (k-color) are combined hierarchically to achieve the full n-color transformation. The transformation process nests multiple smaller transformation operations, where each level transforms a subset of colors, and these nested transformations are composed to achieve the complete color space transformation.
2Manufacturing precision
If high-resolution color transformations are performed with many input colors, then color reproduction accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the processing workload by dividing the transformation of n colors into multiple smaller transformations of k colors each. This segmentation reduces the computational complexity from O(n^d) to O(k^d) per transformation operation, where d is the dimensionality of the color space, enabling high-resolution processing to be completed faster through parallel or sequential composition of smaller operations.
Solution Approach 2:
The patent applies partial transformations by computing transformations for subsets of colors (k out of n) rather than attempting to transform all colors simultaneously. This partial action approach allows the system to achieve sufficient color reproduction accuracy by transforming representative subsets, trading complete simultaneous transformation for faster partial transformations that can be composited.
3Adaptability or versatility
If N-colored transformation tables are created, then complete color combinations are covered, but memory storage requirements become impractical
Solution Approach 1:
The patent segments the comprehensive n-color transformation table into multiple smaller k-color transformation tables. Instead of storing one enormous n-dimensional table requiring exponential memory, the system stores multiple smaller tables for different color subsets, reducing total memory requirements from O(n^d) to O(k^d * C(n,k)) where the segmented structure is more memory-efficient through strategic subset selection and sharing.
Solution Approach 2:
The patent makes smaller k-color transformation tables universal by designing them to be reusable across multiple different color combinations. Each k-color table can serve multiple purposes in different contexts, allowing the same sub-transformation tables to be applied to various subsets of the n colors, reducing the total number of unique tables needed while maintaining complete color combination coverage.
Data Source
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AI summary
In order to implement a computer-aided method for performing color space transformations with high accuracy, such that a result as close as possible to the original can be achieved, where the source color space comprises n colors, the target color space comprises m values, and at least some combinations of the m components of the target color space are assigned combinations of the n components of the target color space via the transformation rule TRV, the following steps are proposed: Select a combination KB of the n colors of the source color space that has a color component FA = qKB(i) > 0 of a color i, and for which the remaining combination of color components is assigned a combination of the target color space, and select two further combinations with a color component q(i) of color i in one where q(i)1 = FA > 0 and in the other where q(i)2 = 0, such that the combination with color component q(i)1 = FA is assigned a combination of the m values of the target color space.which forms the color data set ZI, and to which the combination with color component q(i)2 = 0 is assigned a combination of components that forms the color data set Z2, calculate the ratios which form a set of factors V(1)i = z(1)1/z(1)2, V(2)i = z(2)1/z(2)2, ..., V(m)i = z(m)1/z(m)2, apply the factors V(1)i, V(2)i, ..., V(m)i in transformations by multiplying the z(1)i, z(2)i, ..., z(m)i of the target color space resulting from the transformation with the respective factors V(1)i, V(2)i, ..., V(m)i.