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Konferenzbeitrag

Hierarchical Shape-Adaptive Quantization for Geometry Compression

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44544

Gumhold,  Stefan
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44965

Magnor,  Marcus
Graphics - Optics - Vision, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Zitation

Gumhold, S. (2004). Hierarchical Shape-Adaptive Quantization for Geometry Compression. In Vision, modeling, and visualization 2004 (VMV-04) (pp. 293-298). Berlin, Germany: Akademische Verlagsgesellschaft Aka.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-2AB3-E
Zusammenfassung
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.