Expression-invariant and sparse representation for mesh-based compression for 3-D face models

Junhui Hou, Lap Pui Chau, Ying He, Nadia Magnenat-Thalmann

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Compression of mesh-based 3-D models has been an important issue, which ensures efficient storage and transmission. In this paper, we present a very effective compression scheme specifically for expression variation 3-D face models. Firstly, 3-D models are mapped into 2-D parametric domain and corresponded by expression-invariant parameterizaton, leading to 2-D image format representation namely geometry images, which simplifies the 3-D model compression into 2-D image compression. Then, sparse representation with learned dictionaries via K-SVD is applied to each patch from sliced GI so that only few coefficients and their indices are needed to be encoded, leading to low datasize. Experimental results demonstrate that the proposed scheme provides significant improvement in terms of compression performance, especially at low bitrate, compared with existing algorithms.

Original languageEnglish
Title of host publicationIEEE VCIP 2013 - 2013 IEEE International Conference on Visual Communications and Image Processing
DOIs
Publication statusPublished - Nov 2013
Externally publishedYes
Event2013 IEEE International Conference on Visual Communications and Image Processing, IEEE VCIP 2013 - Kuching, Sarawak, Malaysia
Duration: 17 Nov 201320 Nov 2013

Publication series

NameIEEE VCIP 2013 - 2013 IEEE International Conference on Visual Communications and Image Processing

Conference

Conference2013 IEEE International Conference on Visual Communications and Image Processing, IEEE VCIP 2013
Country/TerritoryMalaysia
CityKuching, Sarawak
Period17/11/1320/11/13

Keywords

  • geometry image
  • K-SVD
  • Mesh model compression
  • parameterization
  • sparse representation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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