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”
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References
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© 2000 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/3-540-45492-6_67
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