by Stefan Gumhold
Abstract:
The compression of polygonal mesh geometry is still an active field of research as in 3d no theoretical bounds are known. This work proposes a geometry coding method based on predictive coding. Instead of using the vertex to vertex distance as distortion measurement, an approximation to the Hausdorffdistance is used resulting in additional degrees of freedom. These are exploited by a new adaptive quantization approach, which is independent of the encoding order. The achieved compression rates are similar to those of entropy based optimization but with a significantly faster compression performance.
Reference:
Hierarchical Shape-Adaptive Quantization for Geometry Compression (Stefan Gumhold), In Proceedings of International Workshop on Vision, Modeling, and Visualization, 2004.
Bibtex Entry:
@INPROCEEDINGS{Gumhold-2004-HSAQFGC,
AUTHOR = {Stefan Gumhold},
TITLE = {Hierarchical Shape-Adaptive Quantization for Geometry Compression},
AFFILIATIONS = {CGV,MPI},
AREAS = {areagp},
BOOKTITLE = {Proceedings of International Workshop on Vision, Modeling, and Visualization},
URL = {http://tu-dresden.de/die_tu_dresden/fakultaeten/fakultaet_informatik/smt/cgv/publikationen/2004/hsaqfgc/hsaqfgc.pdf},
MONTH = {nov},
PAGES = {293--298},
YEAR = {2004},
ABSTRACT = {The compression of polygonal mesh geometry is still an active field of research as in
3d no theoretical bounds are known. This work proposes a geometry coding method based on predictive
coding. Instead of using the vertex to vertex distance as distortion measurement, an approximation
to the Hausdorffdistance is used resulting in additional degrees of freedom. These are exploited by
a new adaptive quantization approach, which is independent of the encoding order. The achieved
compression rates are similar to those of entropy based optimization but with a significantly
faster compression performance.},
ISBN = {3-89838-058-0}
}