TopoSZ: Preserving Topology in Error-Bounded Lossy Compression

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...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics Jg. 30; H. 1; S. 1302 - 1312
Hauptverfasser: Yan, Lin, Liang, Xin, Guo, Hanqi, Wang, Bei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
contour tree
Contours
Critical point
Encoding
Error correction
Image coding
Level set
Lossy compression
MATHEMATICS AND COMPUTING
Quantization (signal)
Scalars
Three-dimensional displays
topological data analysis
Topology
topology in visualization
topology preservation
Trees (mathematics)
ISSN:1077-2626, 1941-0506, 1941-0506
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Zusammenfassung: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). 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.
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National Science Foundation (NSF)
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
SC0023157; SC0022753; SC0021015; NSF IIS 1910733; NSF IIS 2145499; NSF OAC-2311878; NSF OAC-2330367
ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2023.3326920