Improving the Performance of K-Means for Color Quantization

TL;DR

This paper demonstrates that with proper initialization and efficient implementation, k-means clustering can be a highly effective and practical method for color quantization in image processing.

Contribution

It introduces fast, exact variants of k-means with improved initialization strategies for better color quantization performance.

Findings

Efficient k-means variants outperform traditional methods in color quantization.

Proper initialization significantly improves clustering results.

K-means can be a practical choice for color quantization with optimized implementation.

Abstract

Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we investigate the performance of k-means as a color quantizer. We implement fast and exact variants of k-means with several initialization schemes and then compare the resulting quantizers to some of the most popular quantizers in the literature. Experiments on a diverse set of images demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer.