An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps
Joint Authors
Kim, Kwang Baek
Park, Hyun Jun
Cha, Eui-Young
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-14
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Park, Hyun Jun& Kim, Kwang Baek& Cha, Eui-Young. 2016. An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099699
Modern Language Association (MLA)
Park, Hyun Jun…[et al.]. An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1099699
American Medical Association (AMA)
Park, Hyun Jun& Kim, Kwang Baek& Cha, Eui-Young. An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099699
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099699