LiDAR点云压缩下采样与量化参数联合优化建模  

Joint Optimization Modeling of Downsampling and Quantization Parameters for LiDAR Point-Cloud Compression

作　　者：杨先凤[1] 廖陈 段昶[2] 舒惠 来梦军 章超[3] Yang Xianfeng;Liao Chen;Duan Chang;Shu Hui;Lai Mengjun;Zhang Chao(College of Computer Science,Southwest Petroleum University,Chengdu 610500,Sichuan,China;School of Electrical Engineering and Information,Southwest Petroleum University,Chengdu 610500,Sichuan,China;Intelligent Policing Key Laboratory of Sichuan Province,Sichuan Police College,Chengdu 610206,Sichuan,China)

机构地区：[1]西南石油大学计算机科学学院,四川成都610500 [2]西南石油大学电气信息学院,四川成都610500 [3]四川警察学院智能警务四川省重点实验室,四川成都610206

出　　处：《激光与光电子学进展》2024年第14期191-198,共8页Laser & Optoelectronics Progress

基　　金：智能警务四川省重点实验室资助项目(ZNJW2023KFZD003)。

摘　　要：传统的LiDAR点云数据有损压缩方法通常会导致点云点的数量减少和剩余点的坐标精度降低。针对现有点云压缩参数优化方法忽略了点数减少带来的质量损失导致优化效果不高的问题,提出一种LiDAR点云压缩中下采样与量化参数的联合优化建模方法,该方法能同时对两种损失进行优化,提高点云的压缩效率。首先,统计采用不同参数组合压缩点云后的比特流大小;然后,找到码率大小与下采样和量化参数组合之间关系的分析模型,并用模型估计出码率的最小失真和对应的参数组合;最后,根据码率与最小失真对应的参数组合之间的关系建立下采样与量化参数联合优化模型。实验结果表明,所提方法有效提升了点云数据的压缩效率,相比基准编码器,在拟合数据集和测试数据集上分别获得了10.43%和16.39%的BD-rate提升。Conventional LiDAR pointcloud compression methods often lead to a decrease in the total number of points and coordinate accuracy of the remaining points.Addressing the limitations of existing optimization methods for pointcloud compression parameters,which frequently overlook the quality loss associated with reducing the number of points,this paper presents a novel approach for the joint optimization modeling of downsampling and quantization parameters in LiDAR pointcloud compression.This method simultaneously tackles both types of losses,thereby improving the compression efficiency of point clouds.Initially,bitstream sizes resulting from compressing point clouds with various parameter pairs are statistically analyzed.Subsequently,an analytical model is developed to elucidate the relationship between the code rate and the pairs of downsampling and quantization parameters.This model is then employed to estimate the minimum distortion of the code rate and the corresponding parameter pairs.Finally,a joint optimization model for downsampling and quantization parameters is formulated based on the relationship between the code rate and the parameter pairs associated with the minimum distortion.The experimental results indicate that the proposed method effectively improves the compression efficiency of pointcloud data.Compared with the baseline encoder,this method achieves a BDrate improvement of 10.43%on the fit dataset and 16.39%on the test dataset.

关 键 词：LIDAR点云 点云压缩 点云下采样 率失真优化 参数联合优化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]