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Existing error-bounded lossy compression techniques control the pointwiseerror during compression to guarantee the integrity of the decompressed data.However, they typically do not explicitly preserve the topological features indata. When performing post hoc analysis with decompressed data usingtopological methods, preserving topology in the compression process to obtaintopologically consistent and correct scientific insights is desirable. In thispaper, we introduce TopoSZ, an error-bounded lossy compression method thatpreserves the topological features in 2D and 3D scalar fields. Specifically, weaim to preserve the types and locations of local extrema as well as the levelset relations among critical points captured by contour trees in thedecompressed data. The main idea is to derive topological constraints fromcontour-tree-induced segmentation from the data domain, and incorporate suchconstraints with a customized error-controlled quantization strategy from theclassic SZ compressor.Our method allows users to control the pointwise errorand the loss of topological features during the compression process with aglobal error bound and a persistence threshold.

TopoSZ: Preserving Topology in Error-Bounded Lossy Compression