Compression of fMRI data using wavelet tensor train decomposition

Authors

  • P.V. Kharyuk Lomonosov Moscow State University
  • I.V. Оseledets Institute of Numerical Mathematics of RAS (INM RAS)
  • V.L. Ushakov National Research Center «Kurchatov Institute»

Keywords:

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

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.

Author Biographies

P.V. Kharyuk

I.V. Оseledets

V.L. Ushakov

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Published

2014-12-07

How to Cite

Харюк П.В., Оселедец И.В., Ушаков В.Л. Compression of FMRI Data Using Wavelet Tensor Train Decomposition // Numerical methods and programming. 2014. 15. 669-676

Issue

Section

Section 1. Numerical methods and applications