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Lossy Image Compression using Discrete Cosine Transform

Published on March 2012 by Pravin B. Pokle, N. G. Bawane
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2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013)
Foundation of Computer Science USA
NCIPET - Number 2
March 2012
Authors: Pravin B. Pokle, N. G. Bawane
f004cd64-ebb6-4ee4-9cc2-1c03a781251c

Pravin B. Pokle, N. G. Bawane . Lossy Image Compression using Discrete Cosine Transform. 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013). NCIPET, 2 (March 2012), 1-3.

@article{
author = { Pravin B. Pokle, N. G. Bawane },
title = { Lossy Image Compression using Discrete Cosine Transform },
journal = { 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013) },
issue_date = { March 2012 },
volume = { NCIPET },
number = { 2 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 1-3 },
numpages = 3,
url = { /proceedings/ncipet/number2/5197-1009/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013)
%A Pravin B. Pokle
%A N. G. Bawane
%T Lossy Image Compression using Discrete Cosine Transform
%J 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013)
%@ 0975-8887
%V NCIPET
%N 2
%P 1-3
%D 2012
%I International Journal of Computer Applications
Abstract

In recent times the integration of video, audio and data in telecommunication devices has revolutionized the world communication. It has proven to be useful to almost every industry: the corporate world, entertainment industry, multimedia, education and even many household domestic appliances. The major problems encountered with these applications are the high data rates, high bandwidth and large memory required for storage and computing resources. Even with faster internet, throughput rates and improved network infrastructure, there are major bottlenecks in transferring such high volume data through the network due to bandwidth limitations. This justifies the need to develop compression techniques in order to make the best use of available bandwidth [1]. This paper presents how the digital image is compressed using discrete cosine transform and the comparative study with other methods.

References
  1. R. C. Gonzalez and R. E. Woods, "Digital Image Processing", 2nd Ed.,Prentice Hall, 2004.
  2. A. Said and W. A. Peralman, "An Image Multiresolution Representation for Lossless and Lossy Compression," IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 5, NO. 9, SEPTEMBER 1996.
  3. I-Ming Pao and Ming-Ting Sun, Fellow, “Modeling DCT Coefficients For Fast Video Encoding”, IEEE Transactions on circuits and systems For video tech. VOL. 9, NO. 4, JUNE 1999
  4. E. Lam, J. A. Goodman, “A Mathematical Analysis of the DCT Coefficient Distributions for Images” in IEEE Trans. on Image Processing 9(10), 1661-1666 (2000).
  5. A. Skodras, C. Christopoulos, and T. Ebrahimi, "The JPEG2000 Still Image Compression Standard,” IEEE Signal Processing Magazine, pp. 36-58, September 2001.
  6. Shantanu D. Rane and Guillermo Sapiro, Member, IEEE,”Evaluation of JPEG-LS, the New Lossless and Controlled-Lossy Still Image Compression Standard, for Compression of High-Resolution Elevation Data”, IEEE Transactions on Geoscience and Remote sensing, VOL. 39, NO. 10, Oct. 2001
  7. Matteo Frigo and Steven G. Johnson: FFTW, "The Design and Implementation of FFTW3”, Proceedings of the IEEE 93 (2), 216–231 (2005).
Index Terms

Computer Science
Information Sciences

Keywords

JPEG-Joint Photographic Experts Group DCT-discrete cosine transform FFT-Fast Fourier Transform IDCT- Inverse Discrete Cosine Transform