Optimized $k$-means color quantization of digital images in machine-based and human perception-based colorspaces
This work addresses color quantization for digital image processing, providing insights into optimal colorspace selection, but it is incremental as it builds on existing methods without introducing new paradigms.
The study investigated k-means color quantization performance across RGB, CIE-XYZ, and CIE-LUV/CIE-HCL colorspaces on 148 images, finding that RGB performed best in about half of cases, while CIE-XYZ often excelled at higher quantization levels and CIE-LUV at lower levels, with quality assessed using the VIF measure.
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.