TTHRESH: Tensor Compression for Multidimensional Visual Data

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. We introduce a novel lossy compression algorithm for multidimensional data over regular gri...

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Vydané v:	IEEE transactions on visualization and computer graphics Ročník 26; číslo 9; s. 2891 - 2903
Hlavní autori:	Ballester-Ripoll, Rafael, Lindstrom, Peter, Pajarola, Renato
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.09.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Archiving Arithmetic coding Compression algorithms Data compression Data visualization Datasets Encoding higher-order singular value decomposition Mathematical analysis MATHEMATICS AND COMPUTING Matrix decomposition Multidimensional data scientific visualization Singular value decomposition tensor decompositions Tensors Three-dimensional displays Transform-based compression Transforms Tucker model Visualization
ISSN:	1077-2626, 1941-0506, 1941-0506
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Abstract	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. 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.
AbstractList	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. 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.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. 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. 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. 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. 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.
Author	Ballester-Ripoll, Rafael Pajarola, Renato Lindstrom, Peter
Author_xml	– sequence: 1 givenname: Rafael orcidid: 0000-0001-5831-2056 surname: Ballester-Ripoll fullname: Ballester-Ripoll, Rafael email: rballester@ifi.uzh.ch organization: Department of Informatics, University of Zürich, Zürich, Switzerland – sequence: 2 givenname: Peter orcidid: 0000-0003-3817-4199 surname: Lindstrom fullname: Lindstrom, Peter email: pl@llnl.gov organization: Lawrence Livermore National Laboratory, Center for Applied Scientific Computing, Livermore, CA, USA – sequence: 3 givenname: Renato orcidid: 0000-0002-6724-526X surname: Pajarola fullname: Pajarola, Renato email: pajarola@ifi.uzh.ch organization: Department of Informatics, University of Zürich, Zürich, Switzerland
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SubjectTerms	Algorithms Archiving Arithmetic coding Compression algorithms Data compression Data visualization Datasets Encoding higher-order singular value decomposition Mathematical analysis MATHEMATICS AND COMPUTING Matrix decomposition Multidimensional data scientific visualization Singular value decomposition tensor decompositions Tensors Three-dimensional displays Transform-based compression Transforms Tucker model Visualization
Title	TTHRESH: Tensor Compression for Multidimensional Visual Data
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