Information Theory Application in Visualization

Dear Colleagues,    

Information theory is “the science of quantification, coding and communication of information” (Usher, 1984). Since the pioneering work by Shannon and Wiener in the late 1940s, information theory has played an underpinning role in the field of tele- and data communication. It has also been applied to disciplines such as physics, biology, neurology, and psychology. In computer science, its applications include computer graphics, medical imaging, computer vision, data mining, and machine learning. Visualization is concerned with visually coding and communicating information. Many aspects of a visualization pipeline feature events of a probabilistic nature, bearing a striking resemblance to a communication pipeline. This Special Issue of Entropy focuses on the applications of information theory in visualization.

The holistic nature of information-theoretic reasoning has enabled many applications in visualization, including light source placement, view selection in mesh rendering, view selection in volume rendering, focus of attention in volume rendering, multiresolution volume visualization, feature highlighting in unsteady multi-field visualization, feature highlighting in time-varying volume visualization, transfer function design, multimodal data fusion, evaluating isosurfaces, measuring of observation capacity, measuring information content in multivariate data, and confirming the mathematical feasibility of visual multiplexing. Perhaps one of the most exciting applications is the potential to use information theory to underpin the discipline of visualization, i.e., to explain some or all observed phenomena or events in visualization, to provide effective abstraction and quantitative measurements of visual designs and visualization processes, and to enable processes and algorithms for modelling, predicting, and optimizing the effects of visualization.

This Special Issue of Entropy will be co-edited by Mateu Sbert, Min Chen and Han-Wei Shen, co-authors, together with Miquel Feixas, Ivan Viola and Anton Bardera of the recent CRC book, Information Theory for Visualization. Topics of interest include, but are not limited to:

1. Information-theoretic frameworks for

• visualization in general;
• a sub-domain of visualization, such as
  • volume visualization,
  • network visualization,
  • visualization-assisted machine learning,
  • interaction in visualization, and
  • empirical studies in visualization;
• perception and cognition in visualization;
• uncertainty visualization;
• privacy-preserving visualization;
• distribution-based data management and visualization.

2. Information-theoretic metrics in visualization, such as for measuring

• abstraction;
• aesthetics;
• complexity of data, visualization, tasks and user spaces (alphabets);
• cost-benefit of visualization processes;
• distinguishability or similarity of visual objects (e.g., glyphs);
• information preservation (or loss) of visual mapping;
• salience in visualization;
• uncertainty in visualization;
• visualization capacities.

3. Information-theoretic algorithms, such as for

• filtering and selection (e.g., isosurfacing, seeding);
• grouping and clustering (e.g., edge bundling);
• layout (e.g. clutter minimization);
• view optimization;
• feature extraction and tracking;
• time-varying data;
• multivariate visualization;
• in situ visualization;
• ensemble visualisation;
• transfer function design.

Prof. Dr. Mateu  Sbert
Prof. Dr. Min  Chen
Prof. Dr. Han-Wei  Shen
Guest Editors

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