八叉树结构下动态点云的无损几何编码方法  

Lossless Geometry Coding Method for Dynamic Point Clouds under Octree Structure

作　　者：王哲诚 万帅[1,2] 魏磊 杨付正[3] WANG Zhecheng;WAN Shuai;WEI Lei;YANG Fuzheng(School of Electronics and Information,Northwestern Polytechnical University,Xi’an 710129,China;School of Engineering,Royal Melbourne Institute of Technology,Melbourne VIC3001,Australia;School of Telecommunication Engineering,Xidian University,Xi’an 710071,China)

机构地区：[1]西北工业大学电子信息学院,西安710129 [2]皇家墨尔本理工大学工程学院,澳大利亚墨尔本VIC3001 [3]西安电子科技大学通信工程学院,西安710071

出　　处：《西安交通大学学报》2023年第12期11-19,共9页Journal of Xi'an Jiaotong University

基　　金：国家自然科学基金资助项目(62101409,62171353)。

摘　　要：为进一步提高动态点云无损压缩的性能,提出了一种八叉树结构下的几何信息熵编码方法。针对帧内空间相关性,利用当前八叉树节点的已编解码邻域信息,建立帧内邻居节点上下文和帧内邻居父节点上下文。针对帧间时间相关性,将已编解码的上一帧点云作为参考帧,并将参考帧中与当前八叉树节点同位置的八叉树节点作为参考节点。使用参考节点及其父节点进行帧间上下文建模。为充分利用已建模的上下文,并准确地估计不同上下文下当前节点为非空的条件概率,提出了一种基于指数移动平均的二级概率估计方法:分别在帧内邻居节点上下文和帧内邻居父节点上下文下进行概率估计;利用概率估计结果和帧间上下文建模新的二级上下文,并在二级上下文下再次进行概率估计;采用二进制算术编码器实现无损压缩。选取常用的微软体素化人物上半身和8i体素化人物全身数据集进行性能测试,实验结果表明:与近年的方法相比,所提方法的无损压缩性能更高,平均编码增益达到2.2%~28.7%。To further improve the performance of lossless compression of dynamic point clouds,a method of geometry information entropy coding under an octree structure was proposed.By this method,for intra-frame spatial correlation,the contexts of intra-frame neighbor nodes and the contexts of intra-frame neighbor parent nodes are established based on the coded neighborhood information of the current octree node;for inter-frame temporal correlation,the previously coded point cloud frame is employed as the reference frame,and the octree node inside the reference frame at the same position as the current octree node is used as the reference node.The inter-frame context is modeled using the reference node and its parent node.To fully utilize the modeled contexts and accurately estimate the conditional probability that the current node is non-empty under different contexts,a second-level probability estimation method based on the exponential moving average was proposed.By this method,the probability is first estimated under the context of intra-frame neighbor nodes and the context of intra-frame neighbor parent nodes,respectively.Then,a set of second-level contexts are modeled using the inter-frame contexts and the results of the probability estimation,and the probability is estimated again under the second-level contexts.In the end,the lossless compression is realized by the binary arithmetic encoder.To evaluate the compression performance of the proposed method,the commonly used Microsoft voxelized upper bodies(MVUB)and 8i voxelized full bodies(8iVFB)datasets were selected for performance testing.The experimental results show that the proposed method has a higher lossless compression performance than the methods developed recently,with average coding gains of 2.2%to 28.7%.

关 键 词：点云压缩 八叉树 熵编码 算术编码 概率估计 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]