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‘RGB’ Color Image Quantization using Pollination based Optimization

by Gaganpreet Kaur, Dheerendra Singh, Gurjeet Kaur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 9
Year of Publication: 2013
Authors: Gaganpreet Kaur, Dheerendra Singh, Gurjeet Kaur
10.5120/13517-1297

Gaganpreet Kaur, Dheerendra Singh, Gurjeet Kaur . ‘RGB’ Color Image Quantization using Pollination based Optimization. International Journal of Computer Applications. 78, 9 ( September 2013), 18-22. DOI=10.5120/13517-1297

@article{ 10.5120/13517-1297,
author = { Gaganpreet Kaur, Dheerendra Singh, Gurjeet Kaur },
title = { ‘RGB’ Color Image Quantization using Pollination based Optimization },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 9 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number9/13517-1297/ },
doi = { 10.5120/13517-1297 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:08.595499+05:30
%A Gaganpreet Kaur
%A Dheerendra Singh
%A Gurjeet Kaur
%T ‘RGB’ Color Image Quantization using Pollination based Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 9
%P 18-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Color Image Quantization plays an important role for image analysis and visualization. In this paper, RGB color image quantization using pollination based optimization is implemented. The pollination based optimization is applied to RGB color model for image quantization. The Euclidean Distance metric is used for color difference between pixels. Color elimination and reproduction is done by evaluating the Euclidean Distance. Threshold value is taken as the fitness function to calculate the popular and unpopular colors. Color difference calculated using Euclidean Distance and correlate better with visual assessment than color differences calculated using other distance metrics. In order to evaluate the performance of proposed algorithm, MSE (Mean Square Error), Euclidean Distance, Correlation coefficient, PSNR, Time Taken (in seconds) is used. Experimental results shows that MSE values are significantly reduced and we achieve better PSNR and Correlation coefficient values.

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Index Terms

Computer Science
Information Sciences

Keywords

Quantization Segmentation Optimization Pollination Based Optimization Euclidean Distance RGB color Space

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