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Proceedings Paper

Image compression using cascade of neural networks
Author(s): Dimitrios Charalampidis; Chigozie Obiegbu
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Paper Abstract

This paper introduces a novel adaptive cascade architecture for image compression. The idea is an extension of parallel neural network (NN) architectures which have been previously used for image compression. It is shown that the proposed technique results in higher image quality for a given compression ratio than existing NN image compression schemes. It is also shown that training of the proposed architecture is significantly faster than that of other NN-based techniques and that the number of learning parameters is small. This allows the coding process to include adaptation of the learning parameters, thus, compression does not depend on the selection of the training set as in previous single and parallel structure NN.

Paper Details

Date Published: 8 August 2003
PDF: 8 pages
Proc. SPIE 5108, Visual Information Processing XII, (8 August 2003); doi: 10.1117/12.484830
Show Author Affiliations
Dimitrios Charalampidis, Univ. of New Orleans (United States)
Chigozie Obiegbu, Univ. of New Orleans (United States)


Published in SPIE Proceedings Vol. 5108:
Visual Information Processing XII
Zeno J. Geradts; Zia-ur Rahman; Lenny I. Rudin; Robert A. Schowengerdt; Stephen E. Reichenbach, Editor(s)

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