Optimized $k$-means color quantization of digital images in machine-based and human perception-based colorspaces

• URL: http://arxiv.org/abs/2601.19117v2
• Date: Thu, 05 Feb 2026 15:09:16 GMT
• Title: Optimized $k$-means color quantization of digital images in machine-based and human perception-based colorspaces
• Authors: Ranjan Maitra,
• Abstract summary: We investigate the performance of the $k$-means algorithm at four quantization levels in the RGB, CIE-XYZ, and CIE-LUV/CIE-HCL colorspaces.<n>In about half of the cases, $k$-means color quantization is best in the RGB space.<n>There are also some cases, especially at lower $k$, where the best performance is obtained in the CIE-LUV colorspace.
• Score: 2.3859169601259347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Color quantization represents an image using a fraction of its original number of colors while only minimally losing its visual quality. The $k$-means algorithm is commonly used in this context, but has mostly been applied in the machine-based RGB colorspace composed of the three primary colors. However, some recent studies have indicated its improved performance in human perception-based colorspaces. We investigated the performance of $k$-means color quantization at four quantization levels in the RGB, CIE-XYZ, and CIE-LUV/CIE-HCL colorspaces, on 148 varied digital images spanning a wide range of scenes, subjects and settings. The Visual Information Fidelity (VIF) measure numerically assessed the quality of the quantized images, and showed that in about half of the cases, $k$-means color quantization is best in the RGB space, while at other times, and especially for higher quantization levels ($k$), the CIE-XYZ colorspace is where it usually does better. There are also some cases, especially at lower $k$, where the best performance is obtained in the CIE-LUV colorspace. Further analysis of the performances in terms of the distributions of the hue, chromaticity and luminance in an image presents a nuanced perspective and characterization of the images for which each colorspace is better for $k$-means color quantization.

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