APSIPA Transactions on Signal and Information Processing > Vol 14 > Issue 2

Quantization Parameter Cascading for Lossy Point Cloud Attribute Compression in G-PCC

Lei Wei, School of Electronics and Information, Northwestern Polytechnical University, China AND College of Engineering, Xi’an International University, China, Zhiwei Zhu, School of Electronics and Information, Northwestern Polytechnical University, China, zhiweizhu@mail.nwpu.edu.cn , Zhecheng Wang, School of Electronics and Information, Northwestern Polytechnical University, China AND School of Computer Technology and Application, Qinghai University, China, Shuai Wan, School of Electronics and Information, Northwestern Polytechnical University, China AND School of Engineering, Royal Melbourne Institute of Technology, Australia
 
Suggested Citation
Lei Wei, Zhiwei Zhu, Zhecheng Wang and Shuai Wan (2025), "Quantization Parameter Cascading for Lossy Point Cloud Attribute Compression in G-PCC", APSIPA Transactions on Signal and Information Processing: Vol. 14: No. 2, e100. http://dx.doi.org/10.1561/116.20240063

Publication Date: 23 Apr 2025
© 2025 L. Wei et al.
 
Subjects
 
Keywords
point cloud compressionattributeRAHTquantization parameter
 

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In this article:
Introduction 
RAHT related 
RD models with dependencies 
QPC for RAHT 
Experimental results 
Conclusions 
References 

Abstract

Region adaptive hierarchical transform (RAHT) is employed in G-PCC to make attribute compression more efficient. The performance of RAHT is closely related to the quantization parameter (QP), where applying different QPs to different transform depths is beneficial for coding efficiency. In this paper, QP cascading (QPC) is designed based on rate-distortion modelling. Firstly, the single-layer rate-quantization and distortion-quantization models are built by investigating the distribution of residuals. Later, the dependency of adjacent layers is studied to establish the rate-distortion model with dependency. Based on the proposed model, a ratedistortion optimization (RDO) guided QPC (O-QPC) and a fast implementation (F-QPC) are proposed. The experimental results verify the efficiency of the proposed methods. Compared with the G-PCC anchor, under the lossless geometry compression, O-QPC achieves an average of 1.5% performance gain in luma and nearly 13% gain in chroma, and F-QPC achieved an average performance gain of 1.0% in luma and almost 11% in chroma; Under the lossy geometry compression, O-QPC obtained an average of 3.9% gain in luma, and 13% gain in chroma, and F-QPC achieved an average of 3.4% gain in luma and nearly 12% gain in chroma. In particular, F-QPC achieves gains with almost no increase in complexity.

DOI:10.1561/116.20240063

Companion

APSIPA Transactions on Signal and Information Processing Special Issue - Three-dimensional Point Cloud Data Modeling, Processing, and Analysis
See the other articles that are part of this special issue.