By Topic

Image compression using Multilayer Feed Forward Artificial Neural Network with Conjugate Gradient

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Y. Shantikumar Singh ; Dept. of ECE, NIT, Imphal, India ; B. Pushpa Devi ; Kh. Manglem Singh

The performance of Multilayer Feed Forward Artificial Neural Network in image compression using Conjugate Gradient algorithm is examined in this paper. One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. In this paper we presented the algorithm for implementation of digital image compression using MFFANN with 64 input neurons, 13 or 64 neurons in hidden layer that are determining compression rate and 64 output neurons. Based on Conjugate Gradient algorithm compressed for TIF, JPEG, PNG and BMP images. Compression of image in any form is an active field and big business. Image compression is a subset of this huge field of data compression, where we undertake the compression of image data specifically. The performance of compression is evaluated using some standard images. It is shown that the development of architecture and training algorithm provide high compression ratio. The results of simulation are shown and compared different quality parameter of it's by applying on various images.

Published in:

Information and Communication Technologies (WICT), 2012 World Congress on

Date of Conference:

Oct. 30 2012-Nov. 2 2012