通过联合优化进行深度G-PCC几何预处理,使用可差异化的代码代码替代器来提高压缩效率
概括
本研究介绍了一种新的声音化网络,以提高基于几何的点云压缩 (G-PCC) 的效率. 该方法在不改变G-PCC标准的情况下显著降低了数据速率,为用户提高了性能.
科学领域:
- 计算机视觉 计算机视觉
- 数据压缩数据压缩
- 几何建模 几何建模
背景情况:
- 基于几何的点云压缩 (G-PCC) 是一种MPEG标准,提供互操作性,但与深度学习方法相比,压缩效率落后.
- 现有的深度学习方法通常需要对已建立的压缩框架进行重大修改.
研究的目的:
- 为了提高G-PCC的速率扭曲性能,而不会影响其互操作性或计算灵活性.
- 开发一种将深度学习改进与现有的G-PCC标准相结合的方法.
主要方法:
- 提出了一个以压缩为导向的点云声音化网络,并通过可差异化的G-PCC替代模型进行了优化.
- 替代模型接近G-PCC的速率扭曲行为,从而实现端到端的训练.
- 语音化网络使用基于自适应学习的语音化,全球缩放,修剪和点编辑来进行优化.
主要成果:
- 与标准G-PCC相比,实现了平均38.84%的BD率降低.
- 拟议的方法只需要将轻量级的声音化网络添加到G-PCC编码器中,而无需对解码器进行修改.
- 推断不会为最终用户带来任何计算开销.
结论:
- 基于学习的语音化网络的整合为G-PCC标准提供了实际增强.
- 这种方法成功地将经典编解码器和深度学习结合起来,提高了压缩效率,同时保持了向后兼容性.
- 该方法非常适合现实世界的部署场景,需要高效的点云压缩.
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