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Reseach Article

Study of some Non-Linear Transforms for ECG Image Compression

Published on April 2012 by Vibha Aggarwal, Manjeet Singh Patterh
International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012)
Foundation of Computer Science USA
IRAFIT - Number 1
April 2012
Authors: Vibha Aggarwal, Manjeet Singh Patterh
66dcbdba-d3cb-4556-8472-6ca054b6d478

Vibha Aggarwal, Manjeet Singh Patterh . Study of some Non-Linear Transforms for ECG Image Compression. International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012). IRAFIT, 1 (April 2012), 25-28.

@article{
author = { Vibha Aggarwal, Manjeet Singh Patterh },
title = { Study of some Non-Linear Transforms for ECG Image Compression },
journal = { International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012) },
issue_date = { April 2012 },
volume = { IRAFIT },
number = { 1 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 25-28 },
numpages = 4,
url = { /proceedings/irafit/number1/5849-1005/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012)
%A Vibha Aggarwal
%A Manjeet Singh Patterh
%T Study of some Non-Linear Transforms for ECG Image Compression
%J International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012)
%@ 0975-8887
%V IRAFIT
%N 1
%P 25-28
%D 2012
%I International Journal of Computer Applications
Abstract

Out of the many non-linear transform methods the paper is making use of the (i) Essentially Non-Oscillatory cell-average decomposition (ENOCA), (ii) Max-lifting morphological wavelet transform (Maxlift), (iii) Med-lifting morphological wavelet transform (Medlift) and (iv) Morphological Haar wavelet transform (Mhaar) for ECG image compression. This study aims at recreating the image using inverse transforms after compressing the ECG image by making the use of the above mentioned transforms. There is an inversely proportionate relationship between image quality and image compression. This study has been undertaken not for the purpose of quantization and encoding rather an effort is being made to compare the different transform methods for ECG image compression.

References
  1. Kazi Rafiqul Islam, Md. Anwarul Abedin, Masuma Akter, and Rupam Debm. 2011 High Speed ECG Image Compression Using Modified SPIHT. International Journal of Computer and Electrical Engineering, Vol. 3, No. 3, pp 398-402.
  2. Anil K. Jain. 1989 Fundamental of Digital Image Processing. Prentice-Hall Inc.
  3. Andra, K., Chakrabarti, C., and Acharya, T. 2002 A VLSI architecture for lifting-based forward and inverse wavelet transform. IEEE Transactions on Signal Processing, Vol. 50, No.4, pp 966-977.
  4. Gandhi, S. 2005 ENO interpolation for image compression. Thesis of Master of Science.
  5. Gatreuer, P. and Meyer, F. G. 2008 ENO multiresolution schemes with general discretizations. SIAM J. NUMER. ANAL. Vol. 46, no. 6, pp 2953-2977.
  6. Chan, T. F. and Zhou, H.-M. 2001 ENO-wavelet transforms and some applications. Acdemic Press, Inc., pp 1-34.
  7. Heijmans and Goutsias 2000 Nonlinear multiresolution signal decomposition schemes – Part II: Morphological Wavelets. IEEE Transactions on Image Processing, Vol. 9, no. 11, pp 1897-1913.
  8. http://lifeinthefastlane.com/resources/ecg-database/
Index Terms

Computer Science
Information Sciences

Keywords

Essentially Non-oscillatory Cell-average Decomposition (enoca) Max-lifting Morphological Wavelet Transform (maxlift) Med-lifting Morphological Wavelet Transform (medlift) Morphological Haar Wavelet Transform (mhaar) And Ecg Image Compression