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Neuro-Wavelet based Efficient Image Compression using Vector Quantization

by Arun Vikas Singh, Srikanta Murthy K
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 49 - Number 3
Year of Publication: 2012
Authors: Arun Vikas Singh, Srikanta Murthy K
10.5120/7610-0653

Arun Vikas Singh, Srikanta Murthy K . Neuro-Wavelet based Efficient Image Compression using Vector Quantization. International Journal of Computer Applications. 49, 3 ( July 2012), 32-37. DOI=10.5120/7610-0653

@article{ 10.5120/7610-0653,
author = { Arun Vikas Singh, Srikanta Murthy K },
title = { Neuro-Wavelet based Efficient Image Compression using Vector Quantization },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 49 },
number = { 3 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume49/number3/7610-0653/ },
doi = { 10.5120/7610-0653 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:45:22.303457+05:30
%A Arun Vikas Singh
%A Srikanta Murthy K
%T Neuro-Wavelet based Efficient Image Compression using Vector Quantization
%J International Journal of Computer Applications
%@ 0975-8887
%V 49
%N 3
%P 32-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the last few decades, Digital image compression has received significant attention of researchers. Recently, based on wavelets there has been many compression algorithms. In comparison to other compression techniques, image compression using wavelet based algorithms lead to high compression ratios. In this paper, we have proposed a image compression algorithm which combines the feature of both wavelet transform and Radial Basis Function Neural Network along with vector quantization. First the images are decomposed into a set of subbands having different resolution with respect to different frequency bands using wavelet filters. Based on their statistical properties, different coding and quantization techniques are employed. The Differential Pulse Code Modulation (DPCM) is used to compress the low frequency band coefficients and Radial Basis Function Neural Network (RBFNN) is used to compress the high frequency band coefficients. The hidden layer coefficients of RBFNN subsequently are vector quantized so that without much degradation of the reconstructed image, the compression ratio can be increased. In terms of peak signal to noise ratio (PSNR) and computation time (CT), a large compression ratio has been achieved with satisfactory reconstructed images in relation to the existing methods by using the proposed technique.

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Index Terms

Computer Science
Information Sciences

Keywords

Image Compression Radial Basis Function Neural Network Back-Propagation Neural Network Vector Quantization

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