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An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps

Color quantization is an essential technique in color image processing, which has been continuously researched. It is often used, in particular, as preprocessing for many applications. Self-Organizing Map (SOM) color quantization is one of the most effective methods. However, it is inefficient for o...

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Detalles Bibliográficos
Autores principales: Park, Hyun Jun, Kim, Kwang Baek, Cha, Eui-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4738736/
https://www.ncbi.nlm.nih.gov/pubmed/26884748
http://dx.doi.org/10.1155/2016/5302957
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author Park, Hyun Jun
Kim, Kwang Baek
Cha, Eui-Young
author_facet Park, Hyun Jun
Kim, Kwang Baek
Cha, Eui-Young
author_sort Park, Hyun Jun
collection PubMed
description Color quantization is an essential technique in color image processing, which has been continuously researched. It is often used, in particular, as preprocessing for many applications. Self-Organizing Map (SOM) color quantization is one of the most effective methods. However, it is inefficient for obtaining accurate results when it performs quantization with too few colors. In this paper, we present a more effective color quantization algorithm that reduces the number of colors to a small number by using octree quantization. This generates more natural results with less difference from the original image. The proposed method is evaluated by comparing it with well-known quantization methods. The experimental results show that the proposed method is more effective than other methods when using a small number of colors to quantize the colors. Also, it takes only 71.73% of the processing time of the conventional SOM method.
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spelling pubmed-47387362016-02-16 An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps Park, Hyun Jun Kim, Kwang Baek Cha, Eui-Young Comput Intell Neurosci Research Article Color quantization is an essential technique in color image processing, which has been continuously researched. It is often used, in particular, as preprocessing for many applications. Self-Organizing Map (SOM) color quantization is one of the most effective methods. However, it is inefficient for obtaining accurate results when it performs quantization with too few colors. In this paper, we present a more effective color quantization algorithm that reduces the number of colors to a small number by using octree quantization. This generates more natural results with less difference from the original image. The proposed method is evaluated by comparing it with well-known quantization methods. The experimental results show that the proposed method is more effective than other methods when using a small number of colors to quantize the colors. Also, it takes only 71.73% of the processing time of the conventional SOM method. Hindawi Publishing Corporation 2016 2016-01-14 /pmc/articles/PMC4738736/ /pubmed/26884748 http://dx.doi.org/10.1155/2016/5302957 Text en Copyright © 2016 Hyun Jun Park et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Park, Hyun Jun
Kim, Kwang Baek
Cha, Eui-Young
An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps
title An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps
title_full An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps
title_fullStr An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps
title_full_unstemmed An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps
title_short An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps
title_sort effective color quantization method using octree-based self-organizing maps
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4738736/
https://www.ncbi.nlm.nih.gov/pubmed/26884748
http://dx.doi.org/10.1155/2016/5302957
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