Taylor Prediction for Mesh Geometry Compression

In this paper, we introduce a new formalism for mesh geometry prediction. We derive a class of smooth linear predictors from a simple approach based on the Taylor expansion of the mesh geometry function. We use this method as a generic way to compute weights for various linear predictors used for me...

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Vydáno v:	Computer graphics forum Ročník 30; číslo 1; s. 139 - 151
Hlavní autoři:	Courbet, Clément, Hudelot, Céline
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford, UK Blackwell Publishing Ltd 01.03.2011 Wiley
Témata:	Analysis and object representations - Computational Geometry and Object Modeling Compressing Computer graphics Computer Science Geometry geometry compression Graphics I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling-Curve I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Curve, surface, solid, and object representations ‐ Computational Geometry and Object Modeling Image processing systems mesh compression parallelogram prediction solid Studies subdivision surface Wavelet transforms
ISSN:	0167-7055, 1467-8659
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Shrnutí:	In this paper, we introduce a new formalism for mesh geometry prediction. We derive a class of smooth linear predictors from a simple approach based on the Taylor expansion of the mesh geometry function. We use this method as a generic way to compute weights for various linear predictors used for mesh compression and compare them with those of existing methods. We show that our scheme is actually equivalent to the Modified Butterfly subdivision scheme used for wavelet mesh compression. We also build new efficient predictors that can be used for connectivity‐driven compression in place of other schemes like Average/Dual Parallelogram Prediction and High Degree Polygon Prediction. The new predictors use the same neighbourhood, but do not make any assumption on mesh anisotropy. In the case of Average Parallelogram Prediction, our new weights improve compression rates from 3% to 18% on our test meshes. For Dual Parallelogram Prediction, our weights are equivalent to those of the previous Freelence approach, that outperforms traditional schemes by 16% on average. Our method effectively shows that these weights are optimal for the class of smooth meshes. Modifying existing schemes to make use of our method is free because only the prediction weights have to be modified in the code.
Bibliografie:	ark:/67375/WNG-K0BP1J7J-M istex:017A36AC31546CC0E85651D2474791CFE233B088 ArticleID:CGF1838 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0167-7055 1467-8659
DOI:	10.1111/j.1467-8659.2010.01838.x