Multiplexed Image Generation Using Dimensional Gamut Mapping
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Solution Overview
Problem
Existing printing techniques struggle to generate multiplexed images that can be observed in multiple modes using a limited range of colors, as the interdependence of gamuts in different modes complicates straightforward gamut mapping and halftoning processes.
Innovation Solution
The method involves receiving multiple input images, combining them to form a multiplexed input image, and using a multiplexed palette to perform multi-mode gamut mapping, which optimizes color values in a higher-dimensional space to achieve maximum contrast and minimize color reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional gamut mapping is applied to multiplexed images, then color values can be mapped to available printing colors, but the interdependence of gamuts in different modes complicates the process and increases processing complexity
Solution Approach 1:
The patent extends traditional 2D gamut mapping to a higher-dimensional color space that accounts for multiple observation modes simultaneously. By representing colors as points in this extended space with coordinates corresponding to different observation modes, the method captures the interdependence of gamuts across modes and enables comprehensive gamut mapping that preserves color accuracy while systematically managing the increased dimensionality through mathematical transformations.
2Productivity
If a limited color palette is used for laser printing, then the printing process becomes simpler and faster, but the range of reproducible colors is constrained
Solution Approach 1:
The patent transforms the limited discrete color palette into a continuous higher-dimensional color space by introducing additional dimensions corresponding to different observation modes. This parameter expansion allows the system to represent a vastly expanded color gamut using the same physical printing constraints, enabling reproduction of colors that would otherwise require additional inks or materials while maintaining printing speed.
3Reliability
If multiple images are multiplexed to create different appearances in different modes, then security features are enhanced, but the gamut mapping process becomes more complex due to interdependent color spaces
Solution Approach 1:
The patent represents multiplexed image colors as points in a higher-dimensional space where each dimension corresponds to a specific observation mode. This geometric formulation transforms the complex interdependent color space problem into a structured multidimensional coordinate system, allowing systematic gamut mapping through linear transformations and projections that simultaneously satisfy all mode requirements while enhancing security feature robustness.
4Manufacturing precision
If color values are optimized for one observation mode, then contrast is maximized in that mode, but the color values may not be reproducible in other observation modes
Solution Approach 1:
The patent transforms color optimization from a single-mode problem to a multidimensional optimization problem by treating color values as vectors in extended color space. The gamut mapping process optimizes these vectors to simultaneously maximize contrast in each observation mode while ensuring reproducibility across all modes, using mathematical transformations that balance the competing requirements of contrast optimization and cross-mode consistency.
Data Source
AI summary
A method of generating a multiplexed image for printing that can be observed in a plurality of modes includes determining a finite shape of a multiplexed palette in a combined color space. The contrast of the multiplexed image in each mode can be optimized by determining a maximum volume hyperrectangle enclosed in the finite shape. Gamut mapping to this maximum volume hyperrectangle allows for generation of a multiplexed image that has a maximum contrast and is free from artifacts caused by an interdependency between the colors in each mode. Performing a dimensionality reduction using PCA allows for reducing the complexity of the optimization problem.