统一框架实现点云压缩的高效可变率与复杂度支持
UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach
- 设计不均匀分组编码器，按难度动态分配计算资源
- 损失率压缩提升14.02%，无损压缩率提高8.1%
- 单一框架兼顾有损/无损、可变码率与复杂度，适合实际部署
基于学习的点云压缩方法虽性能优异，但存在复杂度高、压缩模式有限、不支持可变码率等问题，限制了实际应用。为此，本文提出轻量级统一点云几何压缩框架UniPCGC。在无损模式中引入不均匀8级无损编码器（UELC），根据编码难度动态分配计算资源，合并低难度组以优化效率；在有损模式中，通过码率调制模块与动态稀疏卷积协同实现可变码率与复杂度（VRCM）。通过动态组合UELC与VRCM，统一框架支持有损压缩、无损压缩、可变码率及可变复杂度。相比当前最优方法，无损压缩率提升8.1%，有损压缩的Bjontegaard Delta Rate（BD-Rate）改善达14.02%，同时具备实用部署能力。
原文摘要 · Abstract (English)
Learning-based point cloud compression methods have made significant progress in terms of performance. However, these methods still encounter challenges including high complexity, limited compression modes, and a lack of support for variable rate, which restrict the practical application of these methods. In order to promote the development of practical point cloud compression, we propose an efficient unified point cloud geometry compression framework, dubbed as UniPCGC. It is a lightweight framework that supports lossy compression, lossless compression, variable rate and variable complexity. First, we introduce the Uneven 8-Stage Lossless Coder (UELC) in the lossless mode, which allocates more computational complexity to groups with higher coding difficulty, and merges groups with lower coding difficulty. Second, Variable Rate and Complexity Module (VRCM) is achieved in the lossy mode through joint adoption of a rate modulation module and dynamic sparse convolution. Finally, through the dynamic combination of UELC and VRCM, we achieve lossy compression, lossless compression, variable rate and complexity within a unified framework. Compared to the previous state-of-the-art method, our method achieves a compression ratio (CR) gain of 8.1\% on lossless compression, and a Bjontegaard Delta Rate (BD-Rate) gain of 14.02\% on lossy compression, while also supporting variable rate and variable complexity.
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