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Num. Meth. Prog., 2014, Volume 15, Issue 4, Pages 669–676 (Mi vmp282)  

Compression of fMRI data using wavelet tensor train decomposition

P. V. Kharyuka, I. V. Oseledetsb, V. L. Ushakovc

a M. V. Lomonosov Moscow State University, Faculty of Computational Mathematics and Cybernetics
b Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow
c National Research Centre "Kurchatov Institute"

Abstract: The application of the Wavelet Tensor Train (WTT) decomposition to the compression of functional magnetic resonance imaging (fMRI) data is considered. Contrary to the classical wavelet transforms, the WTT decomposition is an algebraic technique for the construction of adaptive wavelet transforms, but it requires to store filters for each data array. The WTT method of compressing realistic fMRI data is compared with Daubechies wavelet transforms. The numerical results show that the WTT transform can be successfully used to compress lossy data.

Keywords: numerical tensor methods, Daubechies wavelet transform, wavelet tensor train decomposition, functional magnetic resonance imaging (fMRI) data, lossy data compression.

Full text: PDF file (514 kB)
UDC: 519.6
Received: 05.11.2014

Citation: P. V. Kharyuk, I. V. Oseledets, V. L. Ushakov, “Compression of fMRI data using wavelet tensor train decomposition”, Num. Meth. Prog., 15:4 (2014), 669–676

Citation in format AMSBIB
\Bibitem{KhaOseUsh14}
\by P.~V.~Kharyuk, I.~V.~Oseledets, V.~L.~Ushakov
\paper Compression of fMRI data using wavelet tensor train decomposition
\jour Num. Meth. Prog.
\yr 2014
\vol 15
\issue 4
\pages 669--676
\mathnet{http://mi.mathnet.ru/vmp282}


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