Wavelet transforms are multiresolution
decompositions that can be used to analyze signals and images.
Image compression is one of major applications of wavelet
transforms in image processing. It is considered as one of the most
powerful methods that provides a high compression ratio. However,
its implementation is very time-consuming. At the other hand,
parallel computing technologies are an efficient method for image
compression using wavelets. In this paper, we propose a parallel
wavelet compression algorithm based on quadtrees. We implement
the algorithm using MatlabMPI (a parallel, message passing version
of Matlab), and compute its isoefficiency function, and show that it is
scalable. Our experimental results confirm the efficiency of the
 Jeremy Kepner, "Parallel programming with MatlabMPI", 2002, High
Performance Embedded Computing (HPEC) workshop, MIT Lincoln
Laboratory, Lexington, MA, http://arXiv.org/abs/astro-ph/0107406.
 P. Moravie, H. Essafi, C. Lambert-nebout, and J-L. Basille, "Real-time
image compression using SIMD architectures", In Proceedings of
Computer Architectures for Machine Perception, 1995.
 Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing",
Addison-Wesley Publishing Company.
 S. Khanfir, M. Jemni ,and E. Ben Braiek, "Parallelization of an image
compression and decompression algorithm based on 1D wavelet
transformation", In Proceedings of First International Symposium on
Control, communications and Signal Processing, 2004.
 Shi-xin Sun, Chao-yang Pang, Wen-yu Chen, "A new parallel
architecture for image compression", In Proceedings of CSCW in
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transform domain filters: A spatially selective noise filtration
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 "Message Passing Interface" (MPI), http://www.mpiforum.org