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An Improved Image Compression Method using Vector Quantization for Color Images

Published on September 2015 by Pallavi N. Save, Vishakha Kelkar
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CAE Proceedings on International Conference on Communication Technology
Foundation of Computer Science USA
ICCT2015 - Number 6
September 2015
Authors: Pallavi N. Save, Vishakha Kelkar
8f4daad3-9958-4281-9aa3-29b0592ddb90

Pallavi N. Save, Vishakha Kelkar . An Improved Image Compression Method using Vector Quantization for Color Images. CAE Proceedings on International Conference on Communication Technology. ICCT2015, 6 (September 2015), 10-14.

@article{
author = { Pallavi N. Save, Vishakha Kelkar },
title = { An Improved Image Compression Method using Vector Quantization for Color Images },
journal = { CAE Proceedings on International Conference on Communication Technology },
issue_date = { September 2015 },
volume = { ICCT2015 },
number = { 6 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 10-14 },
numpages = 5,
url = { /proceedings/icct2015/number6/22672-1577/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 CAE Proceedings on International Conference on Communication Technology
%A Pallavi N. Save
%A Vishakha Kelkar
%T An Improved Image Compression Method using Vector Quantization for Color Images
%J CAE Proceedings on International Conference on Communication Technology
%@ 0975-8887
%V ICCT2015
%N 6
%P 10-14
%D 2015
%I International Journal of Computer Applications
Abstract

This paper presents new algorithm based on discrete cosine transform and LBG algorithm using vector quantization for image compression of color images. Vector quantization is mainly divided into three parts i. e Encoding process, Codebook design, Decoding process. In vector quantization generation of codebook is important so that the distortion between the original image and the reconstructed image is minimum. In this RGB component of color image are converted to YCbCr before DCT transform is applied. Y is luminance component; Cb and Cr are chrominance components of the image. This paper compares three different algorithms for vector quantization of color images. It is observed that the performance of new algorithm LBG using DCT is better than LBG. Performance is measured using PSNR and MSE.

References
  1. S Jayaraman, S Esakkirajan, T Veerakumar, Digital Image Processing,2009.
  2. Y. Linde, A. Buzo, and R. M. Gray, "An algorithm for vector quantizer design," IEEE Trans. Commun. , vol. -28, no. 1, pp. 84-95, 1980.
  3. Sadashivappa M. J. , K. V. S Anand Babu, Dr. Srinivas K,"Color Image Compression using SPIHT Algorithm," International Journal of Computer Applications (0975 – 8887),Vol. 16– No. 7, February 2011.
  4. Fouzi Douak, Redha Benzid, Nabil Benoudjit "Color image compression algorithm based on the DCT transform combined to an adaptive block scanning," Int. J. Electron. Commun. (AEU), vol. 65, pp. 16–26, 2011.
  5. Mahmood Shabanifard and Mahrokh G. Shayesteh, "A New Image Compression Method Based on LBG Algorithm in DCT Domain" , IEEE transactions, 2011.
  6. Pallavi N. Save and Vishakha Kelkar, " An Improved Image Compression Method using LBG with DCT", IJERT Journal, Volume-3,Issue-06, June-2014.
  7. Pallavi N. Save and Vishakha Kelkar, "An Improved Image Compression method using LBG with DCT and Fast search", International Conference on Advances in Computing and Information Technology, (ICACIT-2014) ISBN No: 978-93-5107-300-0, Elsevier Publication.
  8. Rafael C Gonzalez, Richard E. Woods, "Digital Image Processing"
Index Terms

Computer Science
Information Sciences

Keywords

Vq Lbg Dct.