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TTHRESH: Tensor Compression for Multidimensional Visual Data

Journal Article · · IEEE Transactions on Visualization and Computer Graphics
 [1];  [2];  [1]
  1. Univ. of Zurich (Switzerland)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing
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Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies. In this paper, we introduce a novel lossy compression algorithm for multidimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to three dimensions and higher, together with bit-plane, run-length and arithmetic coding to compress the HOSVD transform coefficients. Our scheme degrades the data particularly smoothly and achieves lower mean squared error than other state-of-the-art algorithms at low-to-medium bit rates, as it is required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include very fine bit rate selection granularity and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct filtered and/or subsampled versions of all (or selected parts) of the data set.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1644746
Report Number(s):
LLNL-JRNL--750580; 935364
Journal Information:
IEEE Transactions on Visualization and Computer Graphics, Journal Name: IEEE Transactions on Visualization and Computer Graphics Journal Issue: 9 Vol. 26; ISSN 1077-2626
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (2)

Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation preprint January 2021
Compression Challenges in Large Scale Partial Differential Equation Solvers journal September 2019

Figures / Tables (21)

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Related Subjects

97 MATHEMATICS AND COMPUTING
Tucker model
higher-order singular value decomposition
scientific visualization
tensor decompositions
transform-based compression