This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
An Information-theoretic Framework for Visualization
November/December 2010 (vol. 16 no. 6)
pp. 1206-1215
ASCII Text
x 
 
Min Chen, Heike Jaenicke, "An Information-theoretic Framework for Visualization," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1206-1215, November/December, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2010.132,
author = {Min Chen and Heike Jaenicke},
title = {An Information-theoretic Framework for Visualization},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {16},
number = {6},
issn = {1077-2626},
year = {2010},
pages = {1206-1215},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.132},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - An Information-theoretic Framework for Visualization
IS - 6
SN - 1077-2626
SP1206
EP1215
EPD - 1206-1215
A1 - Min Chen,
A1 - Heike Jaenicke,
PY - 2010
KW - Information theory
KW - theory of visualization
KW - quantitative evaluation
VL - 16
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Min Chen, Swansea University
Heike Jaenicke, Ruprecht-Karls-University Heidelberg
In this paper, we examine whether or not information theory can be one of the theoretic frameworks for visualization. We formulate concepts and measurements for qualifying visual information. We illustrate these concepts with examples that manifest the intrinsic and implicit use of information theory in many existing visualization techniques. We outline the broad correlation between visualization and the major applications of information theory, while pointing out the difference in emphasis and some technical gaps. Our study provides compelling evidence that information theory can explain a significant number of phenomena or events in visualization, while no example has been found which is fundamentally in conflict with information theory. We also notice that the emphasis of some traditional applications of information theory, such as data compression or data communication, may not always suit visualization, as the former typically focuses on the efficient throughput of a communication channel, whilst the latter focuses on the effectiveness in aiding the perceptual and cognitive process for data understanding and knowledge discovery. These findings suggest that further theoretic developments are necessary for adopting and adapting information theory for visualization.

[1] R. Ash, Information Theory. John Wiley & Sons, 1965.
[2] U. Bordoloi and H.-W. Shen, View selection for volume rendering. In Proc. IEEE Visualization, pages 487–494, 2005.
[3] K. Brodlie, A classification scheme for scientific visualization. In R. A. Earnshaw, and D. Watson editors, , Animation and Scientific Visualization, pages 125–140. Academic Press, 1993.
[4] S. Bruckner and T. Möller, Isosurface similarity maps. Computer Graphics Forum, 29 (3): 773–782, 2010.
[5] A. Buja, D. Cook, and D. F. Swayne, Interactive high-dimensional data visualization. Journal of Computational and Graphical Statistics, 5: 78–99, 1996.
[6] S. Card and J. Mackinlay, The structure of the information visualization design space. In Proc. IEEE Information Visualization, pages 92–99, 1997.
[7] C. Chen, An information-theoretic view of visual analytics. IEEE Computer Graphics and Applications, 28 (1): 18–23, 2008.
[8] M. Chen, D. Ebert, H. Hagen, R. S. Laramee, R. van Liere, K.-L. Ma, W. Ribarsky, G. Scheuermann, and D. Silver, Data, information and knowledge in visualization. IEEE Computer Graphics and Applications, 29 (1): 12–19, 2009.
[9] E. H. Chi, A taxonomy of visualization techniques using the data state reference model. In Proc. IEEE Information Visualization, pages 69–75, 2000.
[10] M. C. Chuah and S. F. Roth, On the semantics of interactive visualizations. In Proc. IEEE Information Visualization, pages 29–36, 1996.
[11] T. M. Cover and J. A. Thomas, Elements of Information Theory. John Wiley & Sons, 2006.
[12] D. J. Duke, K. W. Brodlie, D. A. Duce, and I. Herman, Do you see what I mean? IEEE Computer Graphics and Applications, 25 (3): 6–9, 2005.
[13] G. Ellis and A. Dix, A taxonomy of clutter reduction for information visualisation. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1216–1223, 2007.
[14] N. Elmqvist and P. Tsigas, A taxonomy of 3d occlusion management for visualization. IEEE Transactions on Visualization and Computer Graphics, 14 (5): 1095–1109, 2008.
[15] M. Feixas, E. del Acebo, P. Bekaert, and M. Sbert, An information theory framework for the analysis of scene complexity. Computer Graphics Forum, 18 (3): 95–106, 1999.
[16] M. Feixas, M. Sbert, and F. González, A unified information-theoretic framework for viewpoint selection and mesh saliency. ACM Transactions on Applied Perception, 6 (1): 1–23, 2009.
[17] S. H. Ferris, Motion parallax and absolute distance. Journal of Experimental Psychology, 95 (2): 258–63, 1972.
[18] D. P. Groth and K. Streefkerk, Provenance and annotation for visual exploration systems. IEEE Transactions on Visualization and Computer Graphics, 12 (6): 1500–1510, 2006.
[19] S. Guiaşu, Information Theory with Applications. McGraw-Hill, 1977.
[20] S. Gumhold, Maximum entropy light source placement. In Proc. IEEE Visualization, pages 275–282, 2002.
[21] M. Gurban and J.-P. Thiran, Information theoretic feature extraction for audio-visual speech recognition. IEEE Transactions on Signal Processing, 57 (12): 4765–4776, 2009.
[22] D. Hankerson, G. A. Harris, and P. D. JohnsonJr., Introduction to Information Theory and Data Compression. CRC Press, 2003.
[23] H. Jänicke and G. Scheuermann, Visual analysis of flow features using information theory. IEEE Computer Graphics and Applications, 30 (1): 40–49, 2010.
[24] H. Jänicke, A. Wiebel, G. Scheuermann, and W. Kollmann, Multifield visualization using local statistical complexity. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1384–1391, 2007.
[25] T. Jankun-Kelly, K.-L. Ma, and M. Gertz, A model and framework for visualization exploration. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 357–369, 2007.
[26] D. A. Keim and H.-P. Kriegel, Visualization techniques for mining large databases: A comparison. IEEE Transactions on Knowledge and Data Engineering, 8 (16): 923–936, 1996.
[27] Z. Liu, N. Nersessian, and J. Stasko, Distributed cognition as a theoretical framework for information visualization. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1173–1180, 2008.
[28] J. A. O'Sullivan, R. E. Blahut, and D. L. Snyder, Information-theoretic image formation. IEEE Transactions on Information Theory, 44 (6): 2094–2123, 1998.
[29] R. Pettersson, Visual Information. Educational Technology Publications, 1993.
[30] D. Pfitzner, V. Hobbs, and D. Powers, A unified taxonomic framework for information visualization. In Proc. Asia-Pacific Symposium on Information Visualisation, pages 57–66, 2003.
[31] H. C. Purchase, N. Andrienko, T. Jankun-Kelly, and M. Ward, Theoretical foundations of information visualization. In A. Kerren, J. T. Stasko, J.-D. Fekete, and C. North editors, , Information Visualization: Human-Centered Issues and Perspectives, Springer LNCS 4950, page 4664, 2008.
[32] P. Rheingans and C. Landreth, Perceptual principles for effective visualizations. In G. Grinstein, and H. Levkowitz editors, Perceptual Issues in Visualization, pages 59–74. Springer-Verlag, 1995.
[33] J. Rigau, M. Feixas, and M. Sbert, Shape complexity based on mutual information. In Proc. IEEE Shape Modeling and Applications, 2005.
[34] J. Rigau, M. Feixas, and M. Sbert, Informational aesthetics measures. IEEE Computer Graphics and Applications, 28 (2): 24–34, 2008.
[35] K. Sayood, Introduction to Data Compression. Morgan Kaufman, 1996.
[36] C. E. Shannon, A mathematical theory of communication. Bell System Technical Journal, 27: 379–423, 1948.
[37] C. E. Shannon, The bandwagon. IRE Transactions on Information Theory, 2 (1): 3, 1956.
[38] C. E. Shannon and W. Weaver, The Mathematical Theory of Communication. University of Illinois Press, 1949.
[39] B. Shneiderman, The eyes have it: A task by data type taxonomy for information visualizations. In Proc. IEEE Symposium on Visual Languages, pages 336–343, 1996.
[40] S. Takahashi and Y. Takeshima, A feature-driven approach to locating optimal viewpoints for volume visualization. In Proc. IEEE Visualization, pages 495–502, 2005.
[41] M. Tory and T. Moller, Rethinking visualization: A high-level taxonomy. In Proc. IEEE Information Visualization, pages 151–158, 2004.
[42] L. Tweedie, Characterizing interactive externalizations. In Proc. ACM CHI, pages 375–382, 1997.
[43] C. Upson, T. FaulhaberJr.,D. Kamins, D. H. Laidlaw, D. Schlegel, J. Vroom, R. Gurwitz, and A. van Dam, The application visualization system: A computational environment for scientific visualization. IEEE Computer Graphics and Applications, 9 (4): 30–42, 1989.
[44] M. J. Usher, Information Theory for Information Technologists. MacMillan, 1984.
[45] J. J. van Wijk, The value of visualization. In Proc. IEEE Visualization, pages 79–86, 2005.
[46] P.-P. Vázquez, M. Feixas, M. Sbert, and W. Heidrich, Automatic view selection using viewpoint entropy and its application to image-based modelling. Computer Graphics Forum, 22 (4): 689–700, 2004.
[47] S. Verdu and S. W. McLaughlin editors. Information Theory: 50 Years of Discovery. IEEE Press, 1999.
[48] I. Viola, M. Feixas, M. Sbert, and M. E. Groller, Importance-driven focus of attention. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 933–940, 2006.
[49] C. Wang and H.-W. Shen, LOD Map - a visual interface for navigating multiresolution volume visualization. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 1029–1036, 2005.
[50] C. Wang, H. Yu, and K.-L. Ma, Importance-driven time-varying data visualization. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1547–1554, 2008.
[51] M. O. Ward and J. Yang, Interaction spaces in data and information visualization. In Proc. Eurographics/IEEE TCVG Symposium on Visualization, pages 137–145, 2004.
[52] S. Wehrend and C. Lewis, A problem-oriented classification of visualization techniques. In Proc. IEEE Visualization, pages 139–143, 1990.
[53] N. Wiener, Cybernetics. John Wiley & Sons, 1948.
[54] J. Yang-Peláez and W. C. Flowers, Information content measures of visual displays. In Proc. IEEE Information Vizualization, pages 99–103, 2000.
[55] J. S. Yi, Y. ah Kang, J. T. Stasko, and J. A. Jacko, Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1224–1231, 2007.
[56] M. X. Zhou and S. K. Feiner, Visual task characterization for automated visual discourse synthesis. In CHI '98: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 392–399, 1998.

Index Terms:
Information theory, theory of visualization, quantitative evaluation
Citation:
Min Chen, Heike Jaenicke, "An Information-theoretic Framework for Visualization," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1206-1215, Nov.-Dec. 2010, doi:10.1109/TVCG.2010.132
Usage of this product signifies your acceptance of the Terms of Use.