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Lossy Image Compression Techniques

by N. Annalakshmi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 20
Year of Publication: 2021
Authors: N. Annalakshmi
10.5120/ijca2021921558

N. Annalakshmi . Lossy Image Compression Techniques. International Journal of Computer Applications. 183, 20 ( Aug 2021), 30-34. DOI=10.5120/ijca2021921558

@article{ 10.5120/ijca2021921558,
author = { N. Annalakshmi },
title = { Lossy Image Compression Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2021 },
volume = { 183 },
number = { 20 },
month = { Aug },
year = { 2021 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number20/32041-2021921558/ },
doi = { 10.5120/ijca2021921558 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:20.884104+05:30
%A N. Annalakshmi
%T Lossy Image Compression Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 20
%P 30-34
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, domain image processing plays a most significant role in real-time applications. Due to reserved bandwidth and capacity, images are needed to be compressed, enhance, and noise-free to reduce the storage space as well as the transmission time. Compression overcomes the problem such as insufficient bandwidth of network and storage of memory device. Nowadays, many techniques are used to compress an image with a good quality. This paper discussed about the concept of image compression and reviews the techniques which are used in lossy image compression. This technique losses some data by using transform codes. Two codes are used to compress an image such as wavelet and fractal. The techniques scalar and vector quantization are used to quantize an input and partition into sub-images. And also it discussed about the 2D-Discrete wavelet transform codes with examples which were done by Matlab software.

References
  1. SukhpalSingh, “Reduction of Blocking Artefacts in JPEG Compressed Image” TECHNOVISION '10, G.N.D.E.C. Ludhiana, Punjab, India- 9th-10th April 2010.
  2. Wei-Yi Wei, “An Introduction to Image Compression”, National Taiwan University, Taipei, Taiwan.
  3. T.Janani, Shennes Mathew, S.Deepa, “Survey of Fractal Image Compression Techniques”, International Journal of Pure and Applied Mathematics, Volume 118 No. 20 2018, 637-643.
  4. Veenadevi. S.V, A.G.Ananth, “Fractal Image Compression of Satellite Imageries”, International Journal of Computer Applications, Volume 30, No.3, September 2011.
  5. Manjari Singh, Sushil Kumar, Siddharth Singh, Manish Shrivastava, “Various Image Compression Techniques: Lossy and Lossless", International Journal of Computer Applications (0975 – 8887) Volume 142 – No.6, May 2016.
  6. Annapurna Misra, Nalini Singh, Boshisottva Dash,“An approach of fractal image compression”, International Journal of Modern Engineering Research (IJMER) Vol. 2, Issue 5, Sept. 2012.
  7. Dinesh V. Rojatkar, Nitesh D. Borkar, Buddhabhushan R. Naik, Ravindra N. Peddiwar, “Image Compression Techniques: Lossy and Lossless”, International Journal of Engineering Research and General Science, Volume 3, Issue 2, March-April, 2015.
  8. Swati Narula, Sunanda Gupta, “Image Compression Radiography using HAAR Wavelet Transform”, International Journal of Computer Applications (0975 – 8887) Volume 117 – No. 18, May 2015.
  9. Sonja Grgic, MislavGrgic,” Performance Analysis of Image Compression Using Wavelets”, IEEE transactions on industrial electronics, Vol. 48, No. 3, June 2001.
  10. Anil. K. Jain, “Fundamentals of digital image processing”, Prentice hall information and system sciences series, Thomas Kailath, Editor.
  11. Miroslav Galabov, “Fractal Image Compression” International Conference on Computer Systems and Technologies - CompSysTech’2003.
  12. K. Sumithra, S. Buvana, R. Somasundaram, “A Survey on Various Types of Image ProcessingTechnique”, International Journal of Engineering Research & Technology (IJERT) Vol. 4 Issue 03, March 2015.
  13. Magdy Bayoumi, Michael Weeks, “Discrete Wavelet Transform-Architectures, Design and performance issues”, Journal of VLSI signal processing, sep. 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Continuous wavelet discrete wavelet scalar vector fractal

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