Fast Color Clustering for Image Preprocessing
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Solution Overview
Problem
Current methods for reducing image precision in machine learning applications, such as K-Means clustering, are either computationally expensive or result in significant loss of image quality, making them unsuitable for real-time processing on low compute devices.
Innovation Solution
A method that applies a cross-product on image pixels to reduce the number of colors and left aligns the result, merging close colors until the target image has exactly K colors, while maintaining image quality and reducing processing time by one order of magnitude compared to traditional K-Means approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional K-Means clustering is used to reduce image precision, then the number of colors is reduced to target levels, but the processing time increases significantly and computational cost becomes excessive
Solution Approach 1:
The patent segments the color reduction process into distinct phases: initial color selection using histogram analysis, cross-product computation for color mapping, and iterative refinement. This segmentation allows each phase to be optimized independently, with histogram-based initial selection providing a fast starting point that avoids the computational burden of traditional K-Means initialization.
Solution Approach 2:
The patent performs preliminary color clustering using histogram analysis and cross-product computation before applying the main reduction algorithm. By pre-computing color frequencies and initial mappings, the system establishes a favorable starting state that reduces the number of iterations needed in subsequent refinement steps, thereby decreasing overall processing time.
2Productivity
If aggressive color reduction is applied to speed up processing, then processing time decreases, but image quality and information retention deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the results of cross-product computations and initial clustering are evaluated, and the process is refined iteratively. The system uses the distribution of colors and frequencies from initial processing to guide subsequent refinement steps, ensuring that information loss is minimized while maintaining processing efficiency.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on image characteristics. The cross-product computation uses variable precision levels, and the number of refinement iterations is adjusted based on the complexity of the color distribution. This adaptive parameter adjustment allows the system to maintain high image quality for complex images while achieving faster processing for simpler cases.
3Use of energy by moving object
If low compute capability devices are used for real-time processing, then device resource consumption decreases, but the ability to perform precision image processing deteriorates
Solution Approach 1:
The patent employs computationally inexpensive operations such as histogram computation, cross-product calculation, and simple color mapping that can be performed efficiently on low-resource devices. These operations replace computationally intensive algorithms like traditional K-Means, enabling precision image processing on devices with limited processing power and memory resources.
Solution Approach 2:
The patent replaces complex iterative optimization mechanisms with more efficient computational approaches. Instead of using heavy numerical optimization algorithms, the system uses histogram-based frequency analysis and cross-product computations that are mathematically equivalent for the purpose of color clustering but computationally much less demanding, enabling deployment on edge devices.
Data Source
AI summary
An approach is provided for color clustering for preprocessing an image. A cross-product on values of pixels in a source image and a number of bits per channel is determined, rounded to integer values, and left aligned to specify a target image. The following actions are repeatedly performed until a count of colors in the target image equals a target: a least frequent color in the target image is identified, distances between the least frequent color and other colors in the target image are determined, a least distance among the distances is determined, where the least distance is between the least frequent color and a closest color, a merged color is generated by merging the least frequent color and the closest color, and the count of the colors in the target image is reduced by replacing the least frequent color and the closest color with the merged color.