Cascaded quantization based progressive 3D mesh compression

Lei Zhang, Xiangyang Ji, Qionghai Dai, Naiyao Zhang
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Abstract

We proposed an efficient progressive 3D mesh compression method supporting flexible quality scalability. In the proposed method, the mesh geometry prediction residuals are partitioned into a number of iterative layers. Each iterative layer is split into several quality layers using cascaded quantization and then encoded by context adaptive arithmetic codec (CABAC). All the quality layers are encoded and transmitted independently to enable better error resilience. To achieve better rate-distortion performance, the quantization parameter of the first quality layer is determined by the importance of the corresponding iterative layer. Simulation results demonstrate that the proposed method is able to provide better compression performance compared to the state-of-the-art coders.
基于级联量化的渐进式三维网格压缩
提出了一种支持柔性质量可扩展性的高效渐进式三维网格压缩方法。该方法将网格几何预测残差划分为多个迭代层。采用级联量化将每个迭代层拆分为多个质量层,然后采用上下文自适应算术编解码器(CABAC)进行编码。所有的质量层都是独立编码和传输的,以实现更好的容错能力。为了获得更好的率失真性能,第一质量层的量化参数由相应迭代层的重要性决定。仿真结果表明,与目前最先进的编码器相比,该方法能够提供更好的压缩性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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