TopoSZ: Preserving Topology in Error-Bounded Lossy Compression
Lin Yan, Xin Liang, Hanqi Guo, Bei Wang

TL;DR
TopoSZ is a novel lossy compression technique that maintains topological features like extrema and contour relations in scalar fields, enabling accurate scientific analysis post-decompression.
Contribution
It introduces a method to preserve topological features during error-bounded lossy compression by integrating topological constraints into the SZ compressor.
Findings
Preserves topological features such as extrema and contour relations.
Allows control over pointwise error and topological loss.
Effective in maintaining data integrity for scientific analysis.
Abstract
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…
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Taxonomy
TopicsTopological and Geometric Data Analysis · Medical Imaging Techniques and Applications · Digital Image Processing Techniques
