Sbert M. and oth. Information Theory Tools for Computer Graphics

Sbert Mateu, Feixas Miquel, Rigau Jaume, Chover Miguel, Viola Ivan. — Morgan and Claypool Publishers, 2009. — 166 p. — (Synthesis Lectures in Computer Graphics and Animation) — ISBN13: 978-1598299298Information theory (IT) tools, widely used in scientific fields such as engineering, physics, genetics, neuroscience, and many others, are also emerging as useful transversal tools in computer graphics. In this book, we present the basic concepts of IT and how they have been applied to the graphics areas of radiosity, adaptive ray-tracing, shape descriptors, viewpoint selection and saliency, scientific visualization, and geometry simplification. Some of the approaches presented, such as the viewpoint techniques, are now the state of the art in visualization. Almost all of the techniques presented in this book have been previously published in peer-reviewed conference proceedings or international journals.Information Theory Basics
Entropy
Relative Entropy and Mutual Information
Inequalities
Entropy Rate
Entropy and Coding
Continuous Channel
Information Bottleneck Method
f-Divergences
Generalized Entropies
Scene Complexity and Refinement Criteria for Radiosity
Background
Scene Information Channel
Scene Complexity
Refinement Criterion based on Mutual Information
Refinement Criteria Based on f-Divergences
Shape Descriptors
Background
Inner Shape Complexity
Outer Shape Complexity
Refinement Criteria for Ray-Tracing
Background
Pixel Quality
PixelContrast
Entropy-Based Supersampling
Entropy-Based Adaptive Sampling
f-Divergences in Adaptive Sampling for Ray-Tracing
Viewpoint Selection and Mesh Saliency
Background
Viewpoint Channel
Viewpoint Similarity and Stability
Best View Selection and Object Exploration
View-based Polygonal Information and Saliency
Importance-driven Viewpoint Selection
View Selection in Scientific Visualization
Adaptation From Polygons to Volumes
Integration of Domain Semantics
Viewpoint-based Geometry Simplification
Background
Viewpoint-Based Error Metric
Simplification Algorithm
Experiments
Summary
Bibliography