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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on visualization and computer graphics Ročník 26; číslo 9; s. 2891 - 2903
Hlavní autoři: Ballester-Ripoll, Rafael, Lindstrom, Peter, Pajarola, Renato
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
AC52-07NA27344; FK-16-012
LLNL-JRNL-750580
USDOE National Nuclear Security Administration (NNSA)
ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2019.2904063