Compression for Large-Scale Time-Varying Volume Data Using Spatio-temporal Features

  • Kun Zhao
  • Naohisa Sakamoto
  • Koji Koyamada
Part of the Communications in Computer and Information Science book series (CCIS, volume 402)


Data compression is always needed in large-scale time-varying volume visualization. In some recent application cases, the compression method is also required to provide a low-cost decompression process. In the present paper, we propose a compression scheme for large-scale time-varying volume data using the spatio-temporal features. With this compression scheme, we are able to provide a proper compression ratio to satisfy many system environments (even a low-spec environment) by setting proper compression parameters. After the compression, we can also provide a low-cost and fast decompression process for the compressed data. Furthermore, we implement a specialized particle-based volume rendering (PBVR) [2] to achieve an accelerated rendering process for the decompressed data. As a result, we confirm the effectiveness of our compression scheme by applying it to the large-scale time-varying turbulent combustion data.


compression time-varying volume data visualization 


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  1. 1.
    Zhao, K., Sakamoto, N., Koyamada, K.: A Volume Compression Scheme Based on Block Division with Fast Cubic B-spline Evaluation. In: Xiao, T., Zhang, L., Fei, M. (eds.) AsiaSim 2012, Part III. CCIS, vol. 325, pp. 373–387. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Kawamura, T., Sakamoto, N., Koyamada, K.: A Level-of-Detail Rendering of a Large-Scale Irregular Volume Dataset Using Particles. Journal of Computer Science and Technology 25(5), 905–915 (2010)CrossRefGoogle Scholar
  3. 3.
    Jang, Y., Ebert, D.S., Gaither, K.: Time-Varying Data Visualization Using Functional Representations. IEEE Transactions on Visualization and Computer Graphics 18(3), 421–433 (2012)CrossRefGoogle Scholar
  4. 4.
    Schnerder, J., Westermann, R.: Compression domain volume rendering. Proceedings of IEEE Visualization, 293–300 (2003)Google Scholar
  5. 5.
    Mensmann, J., Ropinski, T., Hinrichs, K.: A GPU-Supported Loss-less Compression Scheme for Rendering Time-Varying Volume Data. Volume Graphics Eurographics Association, pp. 109–116 (2010)Google Scholar
  6. 6.
    Weiler, M., Botchen, R.P., Stegmeier, S., Ertl, T., Huang, J., Jang, Y., Ebert, D.S., Gaither, K.P.: Hardware-assisted feature analysis of procedurally encoded multifield volumetric data. Computer Graphics and Applications 25(5), 72–81 (2005)CrossRefGoogle Scholar
  7. 7.
    Aps, R., Fetissov, M., Lassen, H.: Smart management of the Baltic Sea fishery system: Myth or reality? In: Baltic International Symposium (BALTIC) 2010 IEEE/OES US/EU, (2010), pp. 1-9. Google Scholar
  8. 8.
    Sohn, B.-S., Bajaj, C., Siddavanahalli, V.: Volumetric video compression for interactive playback. Computer Vision and Image Understanding 96(3), 435–452 (2004)CrossRefGoogle Scholar
  9. 9.
    Wang, C., Gao, J., Li, L., Shen, H.-W.: A Multiresolution Volume Rendering Framework for Large-Scale Time-Varying Data Visualization. In: Proceedings of the International Workshop on Volume Graphics, pp. 11–223 (2005)Google Scholar
  10. 10.
    Shen, H.W., Chiang, L.J., Ma, K.L.: A Fast Volume Rendering Algorithm for Time-Varying Fields Using a Time-Space Partitioning (TSP) Tree. In: IEEE Visualization 1999, pp. 371–377 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kun Zhao
    • 1
  • Naohisa Sakamoto
    • 2
  • Koji Koyamada
    • 2
  1. 1.Graduate School of EngineeringKyoto UniversityJapan
  2. 2.Institute for the Promotion of Excellence in Higher EducationKyoto UniversityJapan

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