Stochastic Color Mapping for Vector Graphic Palette Variation
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Solution Overview
Problem
Conventional image recoloring techniques require manual manipulation and are limited in generating varied, aesthetically pleasing color palettes, leading to inefficient and unaesthetic results, especially in vector graphics.
Innovation Solution
Implementing stochastic color mapping techniques with clustering and de-clustering processes to transfer colors from a source palette to a target graphic, using probabilistic methods like EMD to generate multiple harmonious color palettes while maintaining color relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual manipulation is used for image recoloring, then color control precision is improved, but productivity deteriorates
Solution Approach 1:
The system performs automatic color palette generation and transfer without requiring manual intervention. The stochastic color mapping process autonomously computes color transformations from source to target palettes, and the recoloring engine automatically applies these transformations to the target image, enabling the system to serve itself rather than requiring continuous human operation.
Solution Approach 2:
The patent replaces manual mechanical manipulation with automated computational processes. Instead of manual color selection and adjustment, the system uses stochastic algorithms to generate color palettes and computational methods to transfer colors, substituting human manual operations with automated mechanical/computational systems that achieve both speed and precision.
2Adaptability or versatility
If conventional recoloring techniques are used, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system introduces dynamic randomness into the color palette generation process through stochastic sampling. Instead of using fixed deterministic algorithms, the system employs probabilistic methods that generate different color palettes on each execution, allowing the same source image to produce multiple diverse recoloring results and enabling flexible adaptation to different aesthetic requirements.
Solution Approach 2:
The patent changes the fundamental parameter of color palette generation from deterministic to stochastic. By introducing probability distributions and random sampling into the palette creation process, the system transforms the output characteristics to produce varied and aesthetically diverse color schemes, fundamentally altering how colors are selected and combined in the recoloring process.
3Loss of time
If color clustering is applied, then processing time is reduced, but color precision deteriorates
Solution Approach 1:
The patent segments the color processing task into distinct phases: first clustering colors to reduce the search space, then applying stochastic sampling to generate diverse palettes from the clustered results. This segmentation allows the system to benefit from both the efficiency of clustering and the diversity of stochastic methods without suffering from the precision loss that would result from using clustering alone.
Data Source
AI summary
Recoloring a target graphic using color palettes generated using a stochastic color mapping process. One method of recoloring a target graphic using the stochastic color mapping process includes obtaining a target graphic to be recolored and a source color palette defining source colors for recoloring the target graphic. A target color set of target colors is extracted from the target graphic. The method includes computing a mapping to map source colors of a source color palette to target colors extracted from a target color set of the target graphic based on a transition probability. A destination color palette of destination colors is determined based on the mapping. The target graphic is modified by recoloring at least one object in the target graphic with a destination color from the destination color palette.