MSz: An Efficient Parallel Algorithm for Correcting Morse-Smale Segmentations in Error-Bounded Lossy Compressors

Yuxiao Li, Xin Liang, Bei Wang, Yongfeng Qiu, Lin Yan, Hanqi Guo

Research output: Contribution to journalArticlepeer-review

Abstract

This research explores a novel paradigm for preserving topological segmentations in existing error-bounded lossy compressors. Today's lossy compressors rarely consider preserving topologies such as Morse-Smale complexes, and the discrepancies in topology between original and decompressed datasets could potentially result in erroneous interpretations or even incorrect scientific conclusions. In this paper, we focus on preserving Morse-Smale segmentations in 2D/3D piecewise linear scalar fields, targeting the precise reconstruction of minimum/maximum labels induced by the integral line of each vertex. The key is to derive a series of edits during compression time. These edits are applied to the decompressed data, leading to an accurate reconstruction of segmentations while keeping the error within the prescribed error bound. To this end, we develop a workflow to fix extrema and integral lines alternatively until convergence within finite iterations. We accelerate each workflow component with shared-memory/GPU parallelism to make the performance practical for coupling with compressors.

Original languageEnglish
JournalIEEE Transactions on Visualization and Computer Graphics
DOIs
StateAccepted/In press - 2024

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • feature-preserving compression
  • Lossy compression
  • Morse-Smale segmentations
  • shared-memory parallelism

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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