Advertisement

Mirror, Mirror on the Wall, Who Has the Best Visualization of All? - A Reference Model for Visualization Quality -

  • Helmut Haase
Conference paper
Part of the Eurographics book series (EUROGRAPH)

Abstract

What is a’good’ visualization, one which leads to desired insights? How can we evaluate the quality of a scientific visualization or compare two visualizations (or visualization systems) to each other? In the following, the importance of considering the’visualization context’ is stressed. It consists of the prior knowledge of the user; the aims of the user; the application domain; amount, structure, and distribution of the data; and the available hardware and software. Then, six subqualities are identified: data resolution quality, semantic quality, mapping quality, image quality, presentation and interaction quality, and multi-user quality. The Q vis reference model defines a weight value C (i.e., importance) and a quality value Q for each subquality. The Q vis graph is introduced as a compact, easy to perceive representation of the so-defined- visualization quality. An example illustrates all concepts. The reference model and the graph can help to evaluate visualizations and thus to further improve the quality of scientific visualizations.

Keywords

Reference Model Mapping Quality Space Shuttle Semantic Quality Visualization System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    http://www.avs.com/products/avs5/index.htmlGoogle Scholar
  2. 2.
    Baker, P.: Knowledge-based visualization, IEEE Visualization’92 Workshop on Automated Design of Visualizations, Boston, MA, October 1992Google Scholar
  3. 3.
    Baker, M.P., Bushell, C.: After the Storm: Consiferations for Information Visualization, IEEE Computer Graphics and Applications, May 1995, pp. 12–15Google Scholar
  4. 4.
    Beshers, C., Feiner, S.: Automated Design of Data Visualizations, in: Rosenblum, L., Earnshaw, R., et al. (eds.): Scientific Visualization — Advances and Challenges, Academic Press, London, 1994, pp. 87–102Google Scholar
  5. 5.
    Bryson, S., Levit, C.: The Virtual Windtunnel, IEEE Computer Graphics and Applications, 12, 4, pp. 25–34, 1992CrossRefGoogle Scholar
  6. 6.
    Bryson, S., Levit, C.: Lessons learned while implementing the virtual windtunnel project, Visualization’92, Tutorial # 2, 4.1–4. 7, 1992Google Scholar
  7. 7.
    Foley, J.D., van Dam, A., Feiner, S.K., Hughes, J.F.: Computer graphics: principles and practice, Addison-Wesley, Reading, Mass., 1990Google Scholar
  8. 8.
    Frühauf, T., Göbel, M., Haase, H., Karlsson, K.: Design of a Flexible Monolithic Visualization System, in: Rosenblum, L., Earnshaw, R., et al. (eds.): Scientific Visualization — Advances and Challenges, Academic Press, London, 1994, pp. 265–286Google Scholar
  9. 9.
    Gerfelder, N., Müller, M.: Quality Aspects of Computer-Based Video Services, in: SMPTE, Proceedings of 1994 European SMPTE Conference, Cologne, September 1994, pp. 44–67Google Scholar
  10. 10.
    Gershon, N.: From Perception to Visualization, in: Rosenblum, L., Earnshaw, R., et al. (eds.): Scientific Visualization — Advances and Challenges, Academic Press, London, 1994, pp. 129–139Google Scholar
  11. 11.
    Globus, A., Raible, E.: Fourteen Ways to Say Nothing with Scientific Visualization, IEEE Computer, Vol. 27, No. 7, July 1994, pp. 86–66CrossRefGoogle Scholar
  12. 12.
    Globus, A., Uselton, S.: Evaluation of Visualization Software, Computer Graphics, May 1995, pp. 41–44Google Scholar
  13. 13.
    Haase, H., Dohrmann, C.: Doing It Right: Psychological Tests to Ensure the Quality of Scientific Visualization, in: Göbel, M., David, J., Slavik, P., van Wijk, J.J. (eds): Virtual Environements and Scientific Visualization’96, Springer Verlag, 1996, pp. 243–256Google Scholar
  14. 14.
    Haase, H., Press, T.: Improved Interaction and Visualization of Finite Element Data for Virtual Prototyping, Proc. ASME International Computers in Engineering Conference, Sacramento, USA, September 1997Google Scholar
  15. 15.
    Haase, H.: Symbiosis of Virtual Reality and Scientific Visualization System, Computer Graphics Forum, Vol. 15, No. 3, August 1996Google Scholar
  16. 16.
    Hawse, H.: Evaluating the Quality of Scientific Visualizations: The Q-VIS Reference Model, in: Proc. SPIE Visual Data Exploartion and Analysis V Conference, paper no. 3298-18, San Jose, CA, January 1998Google Scholar
  17. 17.
    Hawse, H., Strassner, J., Dai, F.: Virtual Molecules, Rendering Speed, and Image Quality, in: Göbel, M. (eds.): Virtual Environments’95, Springer Verlag, Wien, 1995, pp. 70–86 and pp. 296–298Google Scholar
  18. 18.
    Hawse, H., Strassner, J., Dai, F.: VR Techniques for the Investigation of Molecule Data, Computers & Graphics, Elsevier Science Ltd., Vol. 20, Nr. 2, 1996, pp. 207–217Google Scholar
  19. 19.
    Hawse, H., Dai, F., Strassner, J., G6bel, M.: Immersive Investigation of Scientific Data, in: Nielson, G., et al. (eds): Scientific Visualization: Overviews, Methodologies & Techniques, IEEE Computer Society Press, 1997Google Scholar
  20. 20.
    Hibbard, W., Dyer, C., Paul, B.: Display of scientific data structures for algorithm visualization, Proceedings IEEE Visualization’92, IEEE Computer Society Press, October 1992, pp. 139–146Google Scholar
  21. 21.
    http://www.igd.ffig.de/www/igd-a4/projects/docs/isvas/Google Scholar
  22. 22.
    Jung, V.: Fuzzy Effectiveness Evaluation for Intelligent User Interfaces to GIS Visualization, Proceedings Fourth ACM Workshop on Advances in Geographic Information Systems, Rockville, Maryland, Nov. 1996, ACM Press, New York, 1996, pp. 157–164Google Scholar
  23. 23.
    Kelley, P.R., Kelley, M.M.: Visual Cues — Practical Data Visualization, IEEE Computer Society Press, Los Alamitos, 1993Google Scholar
  24. 24.
    Mackinley, J: Automatic Design of Graphical Presentations of Relational Information, ACM Transactions on Graphics, Vol. 5, Nr- 2, April 1986, pp. 110–141CrossRefGoogle Scholar
  25. 25.
    Miceli, K., Domik, G.: A visualization framework for multidisciplinary data analysis, IEEE Visualization’92 Workshop on Automated Design of Visualizations, Boston, MA, October 1992Google Scholar
  26. 26.
    Robertson, P.K., De Ferrari, L.: Systematic approaches to visualization: is a reference model needed? in: Rosenblum, L., Earnshaw, R., et al. (eds.): Scientific Visualization — Advances and Challenges, Academic Press, London, 1994, pp. 87–102Google Scholar
  27. 27.
    Robertson, P.K.: A methodology for scientific data visualization: Choosing representations based on a natural scene paradigm, Proceedings IEEE Visualization’90, IEEE Computer Society Press, October 1990, pp. 114–123Google Scholar
  28. 28.
    Rogowitz, B., Treinish, L.: Data structures and perceptual structures, in: Rogowitz, B., Allebach, J. (eds.): SPIE Proceedings: Human Vision, Visual Processing and Digital Display IV, SPIE, Vol. 1913, 1993Google Scholar
  29. 29.
    Robertson, P.K., Silver, D.: Visualization Case Studies: Completing the Loop, IEEE Computer Graphics and Applications, May 1995, pp. 18–19Google Scholar
  30. 30.
    Senay, H., Ignatius, E.: Compositional analysis and synthesis of scientific data visualization techniques, in: Patrikalakis, N.M. (eds.): Scientific Visualization of Physical Phenomena (Proceedings CG International’91), Springer-Verlag, Tokyo, pp. 269–281Google Scholar
  31. 31.
    VandeWettering, M.: The Application Visualization System — AVS 2.0, in: Pixel, July/August 1990, pp. 30–33Google Scholar
  32. 32.
    http://www-sci.nas.nasa.gov:80/Software/VWT/Google Scholar

Copyright information

© Springer-Verlag/Wien 1998

Authors and Affiliations

  • Helmut Haase
    • 1
  1. 1.Fraunhofer Institute for Computer Graphics (IGD)DarmstadtGermany

Personalised recommendations