An Experiment on Volume Data Compression and Visualization using Wavelet Transform

웨이블릿 변환을 이용한 볼륨데이타의 압축 및 가시화 실험

  • Published : 2003.12.01

Abstract

It is not easy that we visualize the large volume data stored in the every client computers of the web environment. One solution is as follows. First we compress volume data, second store that in the database server, third transfer that to client computer, fourth visualize that with direct-volume-rendering in the client computer. In this case, we usually use wavelet transform for compressing large data. This paper reports the experiments for acquiring the wavelet bases and the compression ratios fit for the above processing paradigm. In this experiments, we compress the volume data Engine, CThead, Bentum into 50%, 10%, 5%, 1%, 0.1%, 0.03% of the total data respectively using Harr, Daubechies4, Daubechies12 and Daubechies20 wavelets, then visualize that with direct-volume-rendering, afterwards evaluate the images with eyes and image comparison metrics. When compression ratio being low the performance of Harr wavelet is better than the performance of the other wavelets, when compression ratio being high the performance of Daubechies4 and Daubechies12 is better than the performance of the other wavelets. When measuring with eyes the good compression ratio is about 1% of all the data, when measuring with image comparison metrics, the good compression ratio is about 5-10% of all the data.

웹환경에서 모든 클라이언트 컴퓨터에 방대한 볼륨데이타를 저장하여 놓고 이것을 인터랙티브하게 가시화하는 것은 쉽지 않다. 한 가지 해결 방법은 볼륨데이타를 압축하여 데이타베이스 서버에 보관하여 놓고 요구에 맞추어 네트워크를 통하여 클라이언트 컴퓨터에 전송하여 가시화하는 것이다. 이러한 경우 압축에 많이 사용하는 알고리즘이 웨이블릿 변환이다. 이 논문에서는 서버 컴퓨터에서 볼륨데이타를 웨이블릿 알고리즘을 이용하여 압축하여 놓고 클라이언트 컴퓨터로 전송하여 디렉트볼륨렌더링하는 패러다임에 적합한 웨이블릿과, 압축률을 구하기 위한 실험을 한다. Engine, CThead, Bentum 볼륨데이타를 Har, Daubechies4, 12, 20 웨이블릿을 이용하여 각각 전체 데이타의 50%, 10%, 5%, 1%, 0.1%, 0.03%로 압축하여 디렉트볼륨렌더링을 이용하여 가시화한 후 육안 및 영상평가지표를 이용하여 평가하였다. 성능은 압축률이 낮은 범위에서는 Harr 웨이블릿이 우수하였고 압축률이 높은 곳에서는 Daubechies4와 Daubechies12 웨이블릿이 우수하였다. 바람직한 압축률은 육안으로 평가한 경우는 전체 데이타의 약 1%이고 영상평가지표를 이용하여 평가한 경우는 전체 데이타의 약 5-10%이었다.

Keywords

References

  1. X. Yang and L. Yang, 'A Progressive Wavelet Volume Rendering System,' http://www.cs.uregina.ca/~young/imspiht.htm
  2. E. Klus, S. Ove, E. Christian and E. Thomas, 'Remote 3D Visualization using Image-Streaming Techniques,' Proceedings of the International Symposium on Intelligent Multimedia and Distance Education, 1999
  3. S. G .Mallat, 'A Theory for Multiresolution Signal Decompostion: The Wavelet Representation,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No.7, Jul. 1989, pp. 674-693 https://doi.org/10.1109/34.192463
  4. S. Muraki, 'Volume Data and Wavelet Transforms,' IEEE Computer Graphics & Applications, Vol 13. No.4, Jul. 1993, pp. 50-56 https://doi.org/10.1109/38.219451
  5. L. Linsen, J. T. Gray, V. Pascucci, M. A. Duchaineau, B. Hamann, and K. I. Joy, 'Hierarchial Large-scale Volume Representation with $3\sqrt{2}$ Subdivision and Trivariate B-Spline Wavelets,' Technical Report Number CSE-2002-7, Department of Computer Science, University of California, Davis, 2002
  6. R. Westermann, 'A Multiresolution Framework for Volume Rendering,' ACM Workshop on Volume Visualization, pp 51-57, 1994 https://doi.org/10.1145/197938.197963
  7. H. G. Pagendarm and F. H. Posts, 'Comparative Visualization Approaches and Examples,' Fifth Eurographics Workshops on Visualization in Scientific Computing, Rostock, Germany, May 30 June, 1994
  8. H. Rushmeier, G. Ward, C. Piatko, P. Sanders, and B. Rust, 'Comparing Real and Synthetic Images: Some Ideas About Metrics,' Proceedings of sixth Eurographics Workshop on Rendering, Dublin, Ireland, 1995, pp. 82-91
  9. K. Kim, C. M. Wittenbrink, and A. Pang, 'Extended Specifications and Test Data Sets for Data Level Comparions of direct Volume Rendering Algorithms,' IEEE Transaction on Visualization and Computer Graphics, Vol. 7, No. 4, Oct-Dec 2001, pp. 299-317 https://doi.org/10.1109/2945.965345
  10. C. M. Wittenbrink, A. T. Pang and S. K. Lodha, 'Glyphs for Visualizing Uncertainty in Vector Fields,' IEEE Transaction on Visualization and Computer Graphics, Vol. 2, No.3, Sep 1996 https://doi.org/10.1109/2945.537309
  11. A. S. Glassner, Principles of Digital Image Synthesis, Morgan Kaufman Publishers, Inc. 1995
  12. W. H. Press, S. A. Teukolsky, W. T. Vettering and B. P. Flannery, Numberical Recipes in C, Cambridge University Press, 1992
  13. E. J .Stollnitz, T. D. DeRose and D. H. Salesin, Wavelets for Computer Graphics, Morgan Kaufmann Publishers, Inc. 1996
  14. P. Sabella, 'A Rendering Algorithm for Visualizing 3D Scalar Fields,' ACM SIGGRAPH Computer Graphics, Vol. 22, No. 4, Aug 1988, pp. 160-165 https://doi.org/10.1145/378456.378476
  15. B. Lichtenbelt, R. Crane and S. Naqvi, Introduction to Volume rendering, Prentice Hall PTR, 1998
  16. M. S. Levoy, 'Display of Surfaces from Volume Data,' IEEE Computer Graphics and Applications, Vol. 5, No. 3, May 1988, pp. 29-37 https://doi.org/10.1109/38.511
  17. N. Sahasrabudhe, J. E. West, R. Machiraju, M. Janus, 'Structured Spatial Domain Image and Data COmparison Metrics,'Proc. Visualization'99. pp. 97-104, 1999 https://doi.org/10.1109/VISUAL.1999.809873