Abstract
In this work we propose two parallel algorithms, for image compression, based on multilayer neural networks, by subdividing the image into blocks. The first parallel technique is based on a static distribution of blocks to processors. The advantage of this distribution is that the training phase (construction of the compressor-decompressor network) does not need any communication but its drawback is the load balancing problem. The second parallel technique improves the load balancing problem by using a dynamic distribution of blocks but it requires communication between processors. These two implementations are tested and compared on a distributed memory machine under PVM.
Supported by the European Program INCO-DC, Project “DAPPI”
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
O. Abdel-Wahhab et M.M. Fahmy, “Image Compression using Multilayer Neural Network” IEEE Proc-Vis. Image Signal Process, Vol. 144. No. 5. October 1997
E. M. Daoudi et E.M. Jaara, “ Parallel Methods of Training for Multilayer Neural Network” Euro-Par’99, Lecture Notes in Computer Science 1685, 1999.
H. Nait Charif, “A Fault Tolerant Learning Algorithm for Feedforward Neural Networks” Conférence FTPD 1996, Hawaï.
H. Paugam-moisy, “Réseaux de Neurones Artificiels: Parallélisme, Apprentissage et Modélisation” Habilitation à Diriger des Recherches, ENS-Lyon, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
El Daoudi, M., El Jaâra, M., Cherif, N. (2000). Parallelization of Image Compression on Distributed Memory Architecture. In: Bubak, M., Afsarmanesh, H., Hertzberger, B., Williams, R. (eds) High Performance Computing and Networking. HPCN-Europe 2000. Lecture Notes in Computer Science, vol 1823. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45492-6_67
Download citation
DOI: https://doi.org/10.1007/3-540-45492-6_67
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67553-2
Online ISBN: 978-3-540-45492-2
eBook Packages: Springer Book Archive