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3D Geometry Coding using Mixture Models and the Estimation Quantization Algorithm

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dc.contributor.author Lavu, Sridhar
dc.creator Lavu, Sridhar
dc.date.accessioned 2007-10-31T00:51:11Z
dc.date.available 2007-10-31T00:51:11Z
dc.date.issued 2002-09-01
dc.date.submitted 2002-10-21
dc.identifier.uri http://hdl.handle.net/1911/20050
dc.description Masters Thesis
dc.description.abstract 3D surfaces are used in applications such as animations, 3D object modeling and visualization. The geometries of such surfaces are often approximated using polygonal meshes. This thesis aims to compress 3D geometry meshes by using an algorithm based on normal meshes and the Estimation-Quantization (EQ) algorithm. Normal meshes are multilevel representations where finer level vertices lie in a direction normal to the local surface and therefore compress the vertex data to one scalar value per vertex. A mixture distribution model is used for the wavelet coefficients. The EQ algorithm uses the local neighborhood information and Rate-Distortion optimization to encode the wavelet coefficients. We achieve performance gains of 0.5-1dB compared to the zerotree coder for normal meshes.
dc.subject 3D geometry
normal meshes
EQ coder
geometry processing
dc.subject.other Wavelet based Signal/Image Processing
Multiscale Methods
Multiscale geometry processing
dc.title 3D Geometry Coding using Mixture Models and the Estimation Quantization Algorithm
dc.type Masters Thesis
dc.citation.bibtexName mastersthesis
dc.citation.journalTitle Masters Thesis
dc.date.modified 2004-04-08
dc.contributor.center Digital Signal Processing (http://dsp.rice.edu/)
dc.subject.keyword 3D geometry
normal meshes
EQ coder
geometry processing
dc.identifier.citation S. Lavu, "3D Geometry Coding using Mixture Models and the Estimation Quantization Algorithm," Masters Thesis, 2002.

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  • ECE Publications [1028 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.