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Improved prediction methods for scalable predictive animated mesh compression

机译:用于可伸缩预测动画网格压缩的改进预测方法

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摘要

Animated meshes represented as sequences of static meshes sharing the same connectivity require efficient compression. Among the compression techniques, layered predictive coding methods efficiently encode the animated meshes in a structured way such that the successive reconstruction with an adaptable quality can be performed. The decoding quality heavily depends on how well the prediction is performed in the encoder. Due to this fact, in this paper, three novel prediction structures are proposed and integrated into a state of the art layered predictive coder. The proposed structures are based on weighted spatial prediction with its weighted refinement and angular relations of triangles between current and previous frames. The experimental results show that compared to the state of the art scalable predictive coder, up to 30% bitrate reductions can be achieved with the combination of proposed prediction schemes depending on the content and quantization level.
机译:表示为共享相同连通性的静态网格物体序列的动画网格物体需要有效压缩。在压缩技术中,分层的预测编码方法以结构化的方式有效地对动画网格进行编码,从而可以执行具有自适应质量的连续重建。解码质量很大程度上取决于在编码器中执行预测的程度。由于这个事实,在本文中,提出了三种新颖的预测结构并将其集成到最新的分层预测编码器中。所提出的结构基于加权空间预测及其加权细化和当前帧与先前帧之间的三角形的角度关系。实验结果表明,与现有的可伸缩预测编码器相比,根据内容和量化级别,结合建议的预测方案,可以将比特率降低多达30%。

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