By Topic

Image compression using hybrid neural networks combining the auto-associative multi-layer perceptron and the self-organizing feature map

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)
Abidi, M.A. ; Dept. of Electr. & Comput. Eng., Tennessee Univ., Knoxville, TN, USA ; Yasuki, S. ; Crilly, P.B.

A new image compression technique is presented using hybrid neural networks that combine two different learning networks, the auto-associative multi-layer perceptron (AMLP) and the self-organizing feature map (SOFM). The neural networks simultaneously perform dimensionality reduction with the AMLP and categorization with the SOFM to compress image data. Two hybrid neural networks forming parallel and serial architectures are examined through theoretical analysis and computer simulation. The parallel structure network reduces the dimensionality of input pattern vectors by mapping them to different hidden layers of the AMLP selected by winner-take-all units of the SOFM. The serial structure network categorizes the input pattern vectors into several classes representing prototype vectors. Both the serial and parallel structures are combinations of the AMLP and SOFM networks. These hybrid neural networks achieve clear performance improvement with respect to decoded picture quality and compression ratios, compared to existing image compression techniques

Published in:

Consumer Electronics, IEEE Transactions on  (Volume:40 ,  Issue: 4 )