TopoSZ: Preserving Topology in Error-Bounded Lossy Compression

Yan, Lin; Liang, Xin; Guo, Hanqi; Wang, Bei

doi:10.1109/tvcg.2023.3326920

TopoSZ: Preserving Topology in Error-Bounded Lossy Compression

Journal Article · Mon Nov 06 00:00:00 EST 2023 · IEEE Transactions on Visualization and Computer Graphics

DOI:https://doi.org/10.1109/tvcg.2023.3326920· OSTI ID:2369454

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Argonne National Laboratory (ANL), Argonne, IL (United States); University of Utah
Univ. of Kentucky, Lexington, KY (United States)
The Ohio State Univ., Columbus, OH (United States)
Univ. of Utah, Salt Lake City, UT (United States)

Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in data. When performing post hoc analysis with decompressed data using topological methods, preserving topology in the compression process to obtain topologically consistent and correct scientific insights is desirable. In this paper, we introduce TopoSZ, an error-bounded lossy compression method that preserves the topological features in 2D and 3D scalar fields. Specifically, we aim to preserve the types and locations of local extrema as well as the level set relations among critical points captured by contour trees in the decompressed data. The main idea is to derive topological constraints from contour-tree-induced segmentation from the data domain, and incorporate such constraints with a customized error-controlled quantization strategy from the SZ compressor (version 1.4). In conclusion, our method allows users to control the pointwise error and the loss of topological features during the compression process with a global error bound and a persistence threshold.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Utah, Salt Lake City, UT (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)

Grant/Contract Number:: SC0021015; SC0022753; SC0023157

OSTI ID:: 2369454

Journal Information:: IEEE Transactions on Visualization and Computer Graphics, Journal Name: IEEE Transactions on Visualization and Computer Graphics Journal Issue: 1 Vol. 30; ISSN 1077-2626

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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