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An effective real-time color quantization method based on divisive hierarchical clustering

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Abstract

Color quantization (CQ) is an important operation with many applications in graphics and image processing. Clustering algorithms have been extensively applied to this problem. In this paper, we propose a simple yet effective CQ method based on divisive hierarchical clustering. Our method utilizes the commonly used binary splitting strategy along with several carefully selected heuristics that ensure a good balance between effectiveness and efficiency. We also propose a slightly computationally expensive variant of this method that employs local optimization using the Lloyd–Max algorithm. Experiments on a diverse set of publicly available images demonstrate that the proposed method outperforms some of the most popular quantizers in the literature.

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Acknowledgments

This publication was made possible by grants from the Louisiana Board of Regents (LEQSF2008-11-RD-A-12), US National Science Foundation (0959583, 1117457), and National Natural Science Foundation of China (61050110449, 61073120).

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Authors and Affiliations

  1. Department of Computer Science, Louisiana State University, Shreveport, LA, USA

    M. Emre Celebi

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China

    Quan Wen

  3. Department of Computer Science, University of Illinois, Springfield, IL, USA

    Sae Hwang

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  1. M. Emre Celebi
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  2. Quan Wen
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  3. Sae Hwang
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Correspondence to M. Emre Celebi.

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Celebi, M.E., Wen, Q. & Hwang, S. An effective real-time color quantization method based on divisive hierarchical clustering. J Real-Time Image Proc 10, 329–344 (2015). https://doi.org/10.1007/s11554-012-0291-4

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