Toward Feature-Preserving 2D and 3D Vector Field Compression

Xin Liang, Hanqi Guo, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type change in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress input data together with the error bound field using a modified lossy compressor. Our compression algorithm can be also embarrassingly parallelized for large data handling and in situ processing. We benchmark our method by comparing it with existing lossy compressors in terms of false positive/negative/type rates, compression ratio, and various vector field visualizations with several scientific applications.

Original languageEnglish
Title of host publication2020 IEEE Pacific Visualization Symposium, PacificVis 2020 - Proceedings
EditorsFabian Beck, Jinwook Seo, Chaoli Wang
PublisherIEEE Computer Society
Pages81-90
Number of pages10
ISBN (Electronic)9781728156972
DOIs
Publication statusPublished - Jun 2020
Event13th IEEE Pacific Visualization Symposium, PacificVis 2020 - Tianjin, China
Duration: Apr 14 2020Apr 17 2020

Publication series

NameIEEE Pacific Visualization Symposium
Volume2020-June
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773

Conference

Conference13th IEEE Pacific Visualization Symposium, PacificVis 2020
CountryChina
CityTianjin
Period4/14/204/17/20

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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  • Cite this

    Liang, X., Guo, H., Di, S., Cappello, F., Raj, M., Liu, C., Ono, K., Chen, Z., & Peterka, T. (2020). Toward Feature-Preserving 2D and 3D Vector Field Compression. In F. Beck, J. Seo, & C. Wang (Eds.), 2020 IEEE Pacific Visualization Symposium, PacificVis 2020 - Proceedings (pp. 81-90). [9086223] (IEEE Pacific Visualization Symposium; Vol. 2020-June). IEEE Computer Society. https://doi.org/10.1109/PacificVis48177.2020.6431