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Image Compression: A Comparative Study between ANN and Traditional Approach

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

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

	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}


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.


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