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An Information Theory Framework for the Analysis of Scene Complexity
Author(s) -
Feixas Miquel,
Del Acebo Esteve,
Bekaert Philippe,
Sbert Mateu
Publication year - 1999
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 - 10.1111/1467-8659.00331
Subject(s) - mutual information , visibility , computer science , measure (data warehouse) , theoretical computer science , discretization , computational complexity theory , computation , information theory , algorithm , artificial intelligence , mathematics , data mining , statistics , mathematical analysis , physics , optics
In this paper we present a new framework for the analysis of scene visibility and radiosity complexity. We introduce a number of complexity measures from information theory quantifying how difficult it is to compute with accuracy the visibility and radiosity in a scene. We define the continuous mutual information as a complexity measure of a scene, independent of whatever discretisation, and discrete mutual information as the complexity of a discretised scene. Mutual information can be understood as the degree of correlation or dependence between all the points or patches of a scene. Thus, low complexity corresponds to low correlation and vice versa. Experiments illustrating that the best mesh of a given scene among a number of alternatives corresponds to the one with the highest discrete mutual information, indicate the feasibility of the approach. Unlike continuous mutual information, which is very cheap to compute, the computation of discrete mutual information can however be quite demanding. We will develop cheap complexity measure estimates and derive practical algorithms from this framework in future work.