TopoSZ: Preserving Topology in Error-Bounded Lossy Compression

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 incorporate such constraints with a customized error-controlled quantization strategy from the classic SZ compressor.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.