Zencolor Nesting Cube RGB Mapping for Machine Indexing
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Solution Overview
Problem
The existing RGB color space, particularly the sRGB color cube, is inadequate for aligning color data between physical and digital assets due to its random, redundant, and subjective nature, making it difficult to index color data for machine learning and artificial intelligence.
Innovation Solution
A Zencolor Nesting Cube model is introduced, which reconfigures the sRGB color cube into smaller, individually mapped cubes that eliminate gaps and create defined midpoints, allowing for a more efficient and objective data mapping system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the sRGB color cube model is used to map color data, then color display standardization is achieved, but the color data becomes random, redundant, and subjective making it difficult for machine indexing
Solution Approach 1:
The sRGB color cube is segmented into multiple nested cubes of decreasing sizes. Each cube represents a hierarchical level of color granularity, allowing systematic organization of color data from broad categories to specific shades, eliminating the randomness and redundancy of the original sRGB model while maintaining comprehensive color representation.
Solution Approach 2:
The patent implements a nesting structure where smaller cubes are contained within larger cubes, similar to Russian nesting dolls. This hierarchical nesting organizes color data systematically, with each nested level providing more granular color distinctions, thereby creating a structured and non-redundant color mapping system suitable for machine indexing.
2Adaptability or versatility
If the sRGB color cube with 16,777,216 fixed coordinates is used, then comprehensive color coverage is achieved, but the data redundancy makes it inefficient for machine learning and AI processing
Solution Approach 1:
The vast sRGB color space is segmented into hierarchical cube structures with varying granularities. This segmentation reduces the effective number of distinct color categories needed for processing while maintaining comprehensive color coverage, making the data more manageable for machine learning and AI applications without losing adaptability across different color scenarios.
Solution Approach 2:
Different regions of the color space are represented with different levels of detail through the nested cube structure. Areas with finer color distinctions contain smaller nested cubes, while broader regions use larger cubes, optimizing the data representation to match the actual perceptual and practical requirements of color differentiation.
3Ease of operation
If traditional color mapping methods are used, then color display is achieved, but there are no defined midpoints or clear boundaries for objective data filtering
Solution Approach 1:
The continuous color space is segmented into discrete cubic regions with clearly defined boundaries and midpoints. Each cube provides explicit boundary definitions and central reference points, enabling objective measurement and filtering operations while maintaining visual interpretability through the structured hierarchical organization.
Data Source
AI summary
A computer-implemented method for mapping a RGB digital color space into smaller and more efficient subsets. The smaller subsets are mathematically mapped into a three-dimensional cube model, sides and layers of the cube model become progressively smaller until all sides and layers converge at a center point of the cube model to form a three-dimensional nesting cube. Coordinates in standard RGB digital color space that are not distinguishable to a human eye are consolidated to an appropriated individual mapping cube to provide a color data visualization that is understandable to both a human being and a machine. The three-dimensional nesting cube is organized into equidistant and individual nesting cubes to provide a normalized three-dimensional nesting cube. Each individual cube represents a unique data mapping code of a universal digital small language color model. The normalized three-dimensional nesting cube is mathematically sliced into connecting two-dimensional slices.