Compression-based integral curve data reuse framework for flow visualization

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

    Research output: Contribution to journalArticlepeer-review

    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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