Entropy 2011, 13(1), 254-273; doi:10.3390/e13010254

Information Theory in Scientific Visualization

1 Department of Computer Science, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA 2 Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA
* Author to whom correspondence should be addressed.
Received: 25 November 2010; in revised form: 30 December 2010 / Accepted: 31 December 2010 / Published: 21 January 2011
(This article belongs to the Special Issue Advances in Information Theory)
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Abstract: In recent years, there is an emerging direction that leverages information theory to solve many challenging problems in scientific data analysis and visualization. In this article, we review the key concepts in information theory, discuss how the principles of information theory can be useful for visualization, and provide specific examples to draw connections between data communication and data visualization in terms of how information can be measured quantitatively. As the amount of digital data available to us increases at an astounding speed, the goal of this article is to introduce the interested readers to this new direction of data analysis research, and to inspire them to identify new applications and seek solutions using information theory.
Keywords: information theory; scientific visualization; visual communication channel

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MDPI and ACS Style

Wang, C.; Shen, H.-W. Information Theory in Scientific Visualization. Entropy 2011, 13, 254-273.

AMA Style

Wang C, Shen H-W. Information Theory in Scientific Visualization. Entropy. 2011; 13(1):254-273.

Chicago/Turabian Style

Wang, Chaoli; Shen, Han-Wei. 2011. "Information Theory in Scientific Visualization." Entropy 13, no. 1: 254-273.

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