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Hierarchical Color Quantization Based on Self-organization

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Abstract

In this paper, a new hierarchical color quantization method based on self-organizing maps that provides different levels of quantization is presented. Color quantization (CQ) is a typical image processing task, which consists of selecting a small number of code vectors from a set of available colors to represent a high color resolution image with minimum perceptual distortion. Several techniques have been proposed for CQ based on splitting algorithms or cluster analysis. Artificial neural networks and, more concretely, self-organizing models have been usually utilized for this purpose. The self-organizing map (SOM) is one of the most useful algorithms for color image quantization. However, it has some difficulties related to its fixed network architecture and the lack of representation of hierarchical relationships among data. The growing hierarchical SOM (GHSOM) tries to face these problems derived from the SOM model. The architecture of the GHSOM is established during the unsupervised learning process according to the input data. Furthermore, the proposed color quantizer allows the evaluation of different color quantization rates under different codebook sizes, according to the number of levels of the generated neural hierarchy. The experimental results show the good performance of this approach compared to other quantizers based on self-organization.

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Acknowledgements

This work was supported in part by the Ministry of Science and Innovation of Spain, “Probabilistic Self-Organizing Models for the Restoration of Lossy Compressed Images and Video Project,” under Grant TIN2010- 15351. Also, this work is partially supported by the Ministry of Science and Innovation of Spain under grant TIN2011-24141, project name “Detection of Anomalous Activities in Video Sequences by Self-Organizing Neural systems”.

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  1. Department of Computer Science E.T.S.I. Informatica, University of Malaga, Campus Teatinos s/n, 29071, Malaga, Spain

    Esteban J. Palomo & Enrique Domínguez

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  1. Esteban J. Palomo
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  2. Enrique Domínguez
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Correspondence to Esteban J. Palomo.

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Palomo, E.J., Domínguez, E. Hierarchical Color Quantization Based on Self-organization. J Math Imaging Vis 49, 1–19 (2014). https://doi.org/10.1007/s10851-013-0433-8

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