Compression-based integral curve data reuse framework for flow visualization

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.

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Acknowledgements

This work is supported by NSFC No. 61672055. This work is also partially supported by NSFC Key Project No. 61232012 and the Strategic Priority Research Program - Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Sciences Grant No. XDA05040205.

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Correspondence to Xiaoru Yuan.

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Hong, F., Bi, C., Guo, H. et al. Compression-based integral curve data reuse framework for flow visualization. J Vis 20, 859–874 (2017). https://doi.org/10.1007/s12650-017-0428-4

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Keywords

  • Flow visualization
  • Integral curves
  • Flow lines
  • High performance visualization
  • Data compression
  • Information retrieval