The progressive mesh compression based on meaningful segmentation

Nowadays, both mesh meaningful segmentation (also called shape decomposition) and progressive compression are fundamental important problems, and some compression algorithms have been developed with the help of patch-type segmentation. However, little attention has been paid to the effective combina...

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Published in:	The Visual computer Vol. 23; no. 9-11; pp. 651 - 660
Main Authors:	Cheng, Zhi-Quan, Liu, Hua-Feng, Jin, Shi-Yao
Format:	Journal Article
Language:	English
Published:	Heidelberg Springer Nature B.V 01.09.2007
Subjects:	Algorithms Compression ratio Feature decomposition Finite element method Segmentation
ISSN:	0178-2789, 1432-2315
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Abstract	Nowadays, both mesh meaningful segmentation (also called shape decomposition) and progressive compression are fundamental important problems, and some compression algorithms have been developed with the help of patch-type segmentation. However, little attention has been paid to the effective combination of mesh compression and meaningful segmentation. In this paper, to accomplish both adaptive selective accessibility and a reasonable compression ratio, we break down the original mesh into meaningful parts and encode each part by an efficient compression algorithm. In our method, the segmentation of a model is obtained by a new feature-based decomposition algorithm, which makes use of the salient feature contours to parse the object. Moreover, the progressive compression is an improved degree-driven method, which adapts a multi-granularity quantization method in geometry encoding to obtain a higher compression ratio. We provide evidence that the proposed combination can be beneficial in many applications, such as view-dependent rendering and streaming of large meshes in a compressed form.
AbstractList	Nowadays, both mesh meaningful segmentation (also called shape decomposition) and progressive compression are fundamental important problems, and some compression algorithms have been developed with the help of patch-type segmentation. However, little attention has been paid to the effective combination of mesh compression and meaningful segmentation. In this paper, to accomplish both adaptive selective accessibility and a reasonable compression ratio, we break down the original mesh into meaningful parts and encode each part by an efficient compression algorithm. In our method, the segmentation of a model is obtained by a new feature-based decomposition algorithm, which makes use of the salient feature contours to parse the object. Moreover, the progressive compression is an improved degree-driven method, which adapts a multi-granularity quantization method in geometry encoding to obtain a higher compression ratio. We provide evidence that the proposed combination can be beneficial in many applications, such as view-dependent rendering and streaming of large meshes in a compressed form.
Author	Liu, Hua-Feng Cheng, Zhi-Quan Jin, Shi-Yao
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