比较了k-means在不同色彩空间的图像量化效果，发现高精度时CIE-XYZ更优。
Optimized $k$-means color quantization of digital images in machine-based and human perception-based colorspaces
- 在RGB、CIE-XYZ和CIE-LUV空间中测试k-means量化性能
- 约一半情况下RGB最优，高k值时CIE-XYZ表现更佳
- 适用于图像处理与视觉感知研究者参考
颜色量化通过减少图像颜色数量来压缩数据，同时尽量保持视觉质量。传统上k-means算法多用于基于机器的RGB色彩空间，但近年研究显示其在人类感知色彩空间中表现更优。本文在148张涵盖广泛场景、主题和环境的数字图像上，于四个量化等级（k）下评估了k-means在RGB、CIE-XYZ和CIE-LUV/CIE-HCL色彩空间中的表现。使用视觉信息保真度（VIF）度量量化后图像质量，结果显示约一半情况下RGB空间表现最佳；而在较高量化等级（k）时，CIE-XYZ空间通常更优；低k值时，部分情况中CIE-LUV空间表现最佳。进一步分析图像中色相、色度和亮度分布，揭示了各色彩空间在不同图像特性下的适用性特征。
原文摘要 · Abstract (English)
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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