Fast Color Quantization Using Weighted Sort-Means Clustering

TL;DR

This paper introduces a fast color quantization method based on modified k-means clustering, improving speed and competitiveness with existing methods in image processing tasks.

Contribution

It presents a novel, efficient k-means based color quantization approach with data reduction, sample weighting, and triangle inequality optimization.

Findings

Achieves comparable quality to state-of-the-art methods

Significantly reduces computational time

Demonstrates effectiveness across diverse images

Abstract

Color quantization is an important operation with numerous 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, a fast color quantization method based on k-means is presented. The method involves several modifications to the conventional (batch) k-means algorithm including data reduction, sample weighting, and the use of triangle inequality to speed up the nearest neighbor search. Experiments on a diverse set of images demonstrate that, with the proposed modifications, k-means becomes very competitive with state-of-the-art color quantization methods in terms of both effectiveness and efficiency.