Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 26, Issue 9, Pages 2891-2903Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2019.2904063
Keywords
Transform-based compression; scientific visualization; higher-order singular value decomposition; Tucker model; tensor decompositions
Categories
Funding
- University of Zurich's Forschungskredit Candoc [FK-16-012]
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
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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. 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.
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