Compression-based integral curve data reuse framework for flow visualization

Fan Hong, Chongke Bi, Hanqi Guo, Kenji Ono, Xiaoru Yuan

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Abstract: Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, source-destination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reuse framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data.

Original languageEnglish
Pages (from-to)859-874
Number of pages16
JournalJournal of Visualization
Volume20
Issue number4
DOIs
Publication statusPublished - Nov 1 2017

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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