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Image Compression: A Comparative Study between ANN and Traditional Approach

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Aman Utkarsh, Chandralika Chakraborty
10.5120/ijca2016909145

Aman Utkarsh and Chandralika Chakraborty. Article: Image Compression: A Comparative Study between ANN and Traditional Approach. International Journal of Computer Applications 139(4):27-30, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Aman Utkarsh and Chandralika Chakraborty},
	title = {Article: Image Compression: A Comparative Study between ANN and Traditional Approach},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {4},
	pages = {27-30},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Image compression is a highly essential part of image processing and is a necessity of the modern world required in various fields. It is a process of representing image data using fewer bits than it is required for the original, by performing image compression a certain amount of data used by the image for its storage can be reduced. Compression is necessary in cases where a large amount of data is to be stored or transferred.

This paper reviews some of the conventional methods for achieving Image compression, viz. Run length encoding, DCT, DWT to name a few. Artificial neural networks can also be used to achieve image compression. Here, an attempt is made to compare between the traditional methods of performing image compression and the artificial neural network approach.

References

  1. Manjinder Kaur, Gaganpreet Kaur, “A Survey of Lossless and Lossy Image Compression Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, 2013.
  2. Doaa Mohammed, Fatma Abou-Chadi, “Image Compression Using Block Truncation Coding”, International Journal of Electronics and Computer Science Engineering, 2011.
  3. Bhawna Gautam. May, 2010. Image Compression Using Discrete Cosine Transform & Discrete Wavelet Transform. National Institute of Technology, Rourkela.
  4. Anjana B ,Mrs Shreeeja R “Image compression: an artificial neural network approach”. Vol.2, issue 8, 2012.
  5. Pranob K Charles, Dr. H.Khan, Ch.Rajesh Kumar, N.Nikhita Santhosh Roy, V.Harish,M.Swathi , “Artificial Neural Network based Image Compression using Levenberg- Marquardt Algorithm”, International Journal of Modern Engineering Research (IJMER) , 2013
  6. Venkata Rama Prasad Vaddella, “Artificial neural networks for compression of digital images: a review”, International Journal of Reviews in Computing, 2009-2010.

Keywords

Image Compression, Run-length encoding, DCT, DWT, Levenberg-Marquardt.