Lossy Point Cloud Attribute Compression with Subnode-Based Prediction  

作　　者：YIN Qian ZHANG Xinfeng HUANG Hongyue WANG Shanshe MA Siwei 

机构地区：[1]School of Computer Science,Peking University,Beijing 100871,China [2]School of Computer Science and Technology,University of the Chinese Academy of Sciences,Beijing 100049,China

出　　处：《ZTE Communications》2023年第4期29-37,共9页中兴通讯技术（英文版）

基　　金：supported in part by China Postdoctoral Science Foundation under Grant No.2022M720234;in part by the National Natural Science Foundation under Grant Nos.62071449 and U21B2012.

摘　　要：Recent years have witnessed that 3D point cloud compression(PCC)has become a research hotspot both in academia and industry.Especially in industry,the Moving Picture Expert Group(MPEG)has actively initiated the development of PCC standards.One of the adopted frameworks called geometry-based PCC(G-PCC)follows the architecture of coding geometry first and then coding attributes,where the region adaptive hierarchical transform(RAHT)method is introduced for the lossy attribute compression.The upsampled transform domain prediction in RAHT does not sufficiently explore the attribute correlations between neighbor nodes and thus fails to further reduce the attribute redundancy between neighbor nodes.In this paper,we propose a subnode-based prediction method,where the spatial position relationship between neighbor nodes is fully considered and prediction precision is further promoted.We utilize some already-encoded neighbor nodes to facilitate the upsampled transform domain prediction in RAHT by means of a weighted average strategy.Experimental results have illustrated that our proposed attribute compression method shows better rate-distortion(R-D)performance than the latest MPEG G-PCC(both on reference software TMC13-v22.0 and GeS-TM-v2.0).

关 键 词：point cloud compression MPEG G-PCC RAHT subnode-based prediction 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]