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E-raamat: Information Theory Tools for Visualization

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(The Ohio State University, Columbus, USA), (University of Oxford, United Kingdom), (Vienna University of Technology, Austria), (University of Girona, Spain), (University of Girona, Spain), (University of Girona, Spain)
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The goal is to offer a book on Information theory (IT) tools, which have become state of the art to solve and understand better many of the problems in visualization. This book will cover all relevant literature up to date. This will be the first book solely devoted to this subject, written by leading experts in the field.

Foreword ix
Preface xi
Chapter 1 Basic Concepts of Information Theory
1(14)
1.1 Entropy
2(2)
1.2 Relative Entropy and Mutual Information
4(2)
1.3 Information Specific to a Particular Symbol
6(2)
1.4 Entropy Rate
8(2)
1.5 Jensen--Shannon Divergence
10(1)
1.6 Information Bottleneck Method
11(1)
1.7 Summary
12(3)
Chapter 2 Visualization and Information Theory
15(50)
2.1 Information-Theoretic Measures in Visualization
17(12)
2.1.1 Alphabets and Letters
17(1)
2.1.2 Quantifying Visual Information
18(4)
2.1.3 Information Sources and Communication Channels
22(1)
2.1.3.1 Information Sources
22(2)
2.1.3.2 Channels
24(2)
2.1.4 Visual Coding in Noiseless Channels
26(1)
2.1.5 Visual Coding in Noisy Channels
27(2)
2.2 Information-Theoretic Laws for Visualization
29(3)
2.3 Information-Theoretic Process Optimization
32(17)
2.3.1 Data Processing Inequality
34(1)
2.3.2 Transformation of Alphabets in a Visualization Process
34(2)
2.3.3 Cost--Benefit Measures for Visualization Processes
36(5)
2.3.4 Examples of Cost--Benefit Analysis
41(1)
2.3.4.1 Interaction in Visualization
42(3)
2.3.4.2 Disseminative Visualization
45(1)
2.3.4.3 Observational Visualization
46(1)
2.3.4.4 Analytical Visualization
47(1)
2.3.4.5 Model-Developmental Visualization
48(1)
2.4 Information-Theoretic Links: Visualization and Perception
49(15)
2.4.1 Visual Multiplexing
49(1)
2.4.1.1 Multiplexing in Communication
49(1)
2.4.1.2 Multiplexing in Visualization
49(6)
2.4.1.3 Multiplexing and Information-Theoretic Measures
55(2)
2.4.2 Information-Theoretic Quality Metrics
57(1)
2.4.2.1 Differentiability of Glyphs
58(1)
2.4.2.2 Hamming Distance
58(2)
2.4.2.3 Quasi-Hamming Distance for Glyph Design
60(3)
2.4.3 User Studies in the Information-Theoretic Framework
63(1)
2.5 Summary
64(1)
Chapter 3 Viewpoint Metrics and Applications
65(24)
3.1 Viewpoint Metrics and Basic Applications
65(1)
3.2 From Polygons to Volumes
66(5)
3.2.1 Isosurfaces
66(1)
3.2.2 Volumetric Data
67(4)
3.3 Visibility Channel in Volume Visualization
71(4)
3.3.1 Visibility Channel
71(3)
3.3.2 Voxel Information
74(1)
3.4 Importance-Driven Focus of Attention
75(4)
3.5 Viewpoint Selection Using Voxel Information
79(1)
3.6 Application to Illustrative Rendering
80(6)
3.6.1 Ambient Occlusion
80(6)
3.6.2 Color Ambient Occlusion
86(1)
3.7 Summary
86(3)
Chapter 4 Volume Visualization
89(36)
4.1 Time-Varying Data
90(5)
4.2 Level-of-Detail Characterization
95(2)
4.3 Isosurfaces
97(7)
4.4 Splitting
104(2)
4.5 Transfer Function Design
106(8)
4.6 Multimodal Volume Visualization
114(9)
4.7 Summary
123(2)
Chapter 5 Flow Visualization
125(24)
5.1 Complexity Measure of Vector Fields
126(3)
5.1.1 Entropy Field
127(2)
5.2 Complexity Measures of Streamlines
129(5)
5.2.1 Information Complexity of Streamlines
129(2)
5.2.2 Geometric Complexity of Streamlines
131(1)
5.2.2.1 Construction of Histograms
131(2)
5.2.3 Streamline Segmentation
133(1)
5.3 Information-Aware Streamline Seeding
134(9)
5.3.1 View-Independent Method
134(1)
5.3.1.1 Initial Seeding
134(1)
5.3.1.2 Importance-Based Seed Insertion
134(2)
5.3.1.3 Redundant Streamline Pruning
136(1)
5.3.1.4 Reconstructing a Vector Field from Streamlines
136(1)
5.3.1.5 Seed Selection Result
137(1)
5.3.2 View-Dependent Method
138(1)
5.3.2.1 Maximal Entropy Projection (MEP)
138(1)
5.3.2.2 Seed Placement
139(2)
5.3.2.3 Entropy-Based Streamline Selection
141(1)
5.3.2.4 Finding Optimal Views
142(1)
5.4 Information Channels for Flow Visualization
143(3)
5.5 Summary
146(3)
Chapter 6 Information Visualization
149(28)
6.1 Theoretical Foundations of Information Visualization
151(1)
6.2 Quality Metrics for Data Visualization
152(1)
6.3 It Metrics for Parallel Coordinates
153(3)
6.4 Maximum Entropy Summary Trees
156(3)
6.4.1 How to Construct a Maximum Entropy Summary Tree
159(1)
6.5 Multivariate Data Exploration
159(5)
6.5.1 Variable Entropy
160(1)
6.5.2 Mutual Information between Variables
161(2)
6.5.3 Specific Information
163(1)
6.6 Time-Varying Multivariate Data
164(3)
6.6.1 Transfer Entropy
165(2)
6.6.2 Visualization of Information Transfer
167(1)
6.7 Privacy and Uncertainty
167(3)
6.7.1 Metrics for Uncertainty
168(2)
6.8 Mutual Information Diagram
170(5)
6.8.1 The Taylor Diagram
171(1)
6.8.2 Mutual Information Diagram
172(3)
6.8.3 Properties of the MI Diagram
175(1)
6.9 Summary
175(2)
Bibliography 177(14)
Index 191
Min Chen developed his academic career in Wales between 1984 and 2011. He is currently the professor of scientific visualization at Oxford University and a fellow of Pembroke College. His research interests include visualization, computer graphics and human-computer interaction. His services to the research community include papers co-chair of IEEE Visualization 2007 and 2008, Eurographics 2011, IEEE VAST 2014; co-chair of Volume Graphics 1999 and 2006, EuroVis 2014; associate editor-in-chief of IEEE Transactions on Visualization and Computer Graphics; and co-director of Wales Research Institute of Visual Computing. He is a fellow of British Computer Society, European Computer Graphics Association, and Learned Society of Wales.



Miquel Feixas is an associate professor in Computer Science at the University of Girona. He received an M.Sc. in Theoretical Physics (1979) at the Universitat Autònoma de Barcelona and a Ph.D. in Computer Science (2002) at the Universitat Politècnica de Catalunya. His research is focused on the application of information theory techniques to global illumination, viewpoint selection, image processing, and scientific visualization. He has co-authored two books and more than eighty papers in his areas of research.



Ivan Viola is Assistant Professor at the Vienna University of Technology, Austria and Adjunct Professor at University of Bergen, Norway. He received M.Sc. in 2002 and Ph.D. in 2005 from Vienna University of Technology, Austria. His research is focusing on effective visualization methods that are well understandable by humans. This includes illustrative visualization and perceptually-driven visualization driven by statistical or information theoretic methods.



Anton Bardera is a lecturer in Computer Science at the University of Girona. He received a M.Sc. in Telecommunications engineering (2002) at the Universitat Politècnica de Catalunya and a Ph.D. in Computer Science (2008) at the University of Girona. His research interests include image processing, scientific visualization, information theory, and biomedical applications. He has co-authored one book, 15 journal papers, and many conference papers in his areas of interest.





Han-Wei Shen is a full professor at The Ohio State University. He received his BS degree from Department of Computer Science and Information Engineering at National Taiwan University in 1988, the MS degree in computer science from the State University of New York at Stony Brook in 1992, and the PhD degree in computer science from the University of Utah in 1998. From 1996 to 1999, he was a research scientist at NASA Ames Research Center in Mountain View California. His primary research interests are
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