In this work, a novel three-dimensional (3d) meshsequencecompression amework suitable for progressive streaming is described. The proposed approach first implements a temporal frame-clustering algorithm based on the...
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ISBN:
(纸本)9781538626368
In this work, a novel three-dimensional (3d) meshsequencecompression amework suitable for progressive streaming is described. The proposed approach first implements a temporal frame-clustering algorithm based on the curvature of pivot vertex trajectory. Then, a decorrelation method is used to remove the redundancy of data in x. y, and z coordinates. Next, to reduce the amount of meshsequencedata, the vertex motion trajectory data in each cluster is compressed using principal component analysis (PCA). Further, the coefficients of x, and z coordinates obtained from different principal components are considered as mesh signals, which are processed by a spectral graph wavelet transform(SGWT). Finally, the obtained wavelet coefficients are encoded using CSPECK. By transmitting data on different hit-planes from encoder, a 31) meshsequence is encoded into a multi-resolution sequence. Experimental results show that the proposed method can realize progressive streaming of meshsequence. Furthermore, the results also show that the proposed approach outperforms state-of-the-art methods in terms of storage space requirement and minimizing the reconstruction error.
dynamic meshsequence (dMS) is a simple and accurate representation for precisely recording a 3d animation sequence. despite its simplicity, this representation is typically large in data size, making storage and tran...
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dynamic meshsequence (dMS) is a simple and accurate representation for precisely recording a 3d animation sequence. despite its simplicity, this representation is typically large in data size, making storage and transmission expensive. This paper presents a novel framework that allows effective dMS compression and progressive streaming by eliminating spatial and temporal redundancy. To explore temporal redundancy, we propose a temporal frame-clustering algorithm to organize dMS frames by their motion trajectory changes, eliminating intracluster redundancy by principal component analysis dimensionality reduction. To eliminate spatial redundancy, we propose an algorithm to transform the coordinates of mesh vertex trajectory into a decorrelated trajectory space, generating a new spatially nonredundant trajectory representation. We finally apply a spectral graph wavelet transform with color set partitioning embedded block encoding to turn the resultant dMS into a multiresolution representation to support progressive streaming. Experiment results show that our method outperforms several existing methods in terms of storage requirement and reconstruction quality.
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