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An Information‐Theoretic Observation Channel for Volume Visualization
Author(s) -
Bramon R.,
Ruiz M.,
Bardera A.,
Boada I.,
Feixas M.,
Sbert M.
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12128
Subject(s) - visualization , computer science , channel (broadcasting) , set (abstract data type) , mutual information , data mining , information transfer , metric (unit) , measure (data warehouse) , information visualization , transfer function , data visualization , pixel , transfer (computing) , information theory , viewpoints , artificial intelligence , mathematics , statistics , computer network , telecommunications , operations management , parallel computing , electrical engineering , economics , programming language , engineering , art , visual arts
Different quality metrics have been proposed in the literature to evaluate how well a visualization represents the underlying data. In this paper, we present a new information‐theoretic framework that quantifies the information transfer between the source data set and the rendered image. This approach is based on the definition of an observation channel whose input and output are given by the intensity values of the volumetric data set and the pixel colors, respectively. From this channel, the mutual information, a measure of information transfer or correlation between the input and the output, is used as a metric to evaluate the visualization quality. The usefulness of the proposed observation channel is illustrated with three fundamental visualization applications: selection of informative viewpoints, transfer function design, and light positioning.