APSIPA Transactions on Signal and Information Processing > Vol 11 > Issue 1

Content-Adaptive Level of Detail for Lossless Point Cloud Compression

Lei Wei, School of Electronics and Information, Northwestern Polytechnical University, China, l.wei@mail.nwpu.edu.cn , Shuai Wan, School of Electronics and Information, Northwestern Polytechnical University, China and School of Engineering, Royal Melbourne Institute of Technology, Australia, Fuzheng Yang, School of Telecommunications Engineering, Xidian University, China, Zhecheng Wang, School of Electronics and Information, Northwestern Polytechnical University, China
 
Suggested Citation
Lei Wei, Shuai Wan, Fuzheng Yang and Zhecheng Wang (2022), "Content-Adaptive Level of Detail for Lossless Point Cloud Compression", APSIPA Transactions on Signal and Information Processing: Vol. 11: No. 1, e23. http://dx.doi.org/10.1561/116.00000004

Publication Date: 28 Jul 2022
© 2022 L. Wei, S. Wan, F. Yang and Z. Wang
 
Subjects
 
Keywords
Point cloud compressionlevel of detailprediction errorbitrate
 

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In this article:
Introduction 
Related Works 
LOD in G-PCC 
Content-adaptive LOD with Optimization 
Experimental Results and Discussions 
Conclusions 
References 

Abstract

The nonuniform distribution of points in a point cloud and their abundant attribute information (such as colour, reflectance, and normal) result in the generation of massive data, making point cloud compression (PCC) essential for related applications. The hierarchical structure of the level of detail (LOD) in a point cloud and the corresponding predictions are commonly used in PCC, whereas the current method of LOD generation is neither content adaptive nor optimized. Targeting lossless PCC, an LOD prediction error model is proposed in this work, based on which the prediction error is minimized to obtain the optimal coding performance. As a result, the process of generating LOD is optimized, where the smallest number of LOD levels that yields the minimum attribute bitrate can be found. The proposed method is evaluated on various standard datasets under common test conditions. Experimental results show that the proposed method achieves optimal coding performance in a content-adaptive way while significantly reducing the time required for encoding and decoding, i.e., ~15.2% and ~17.3% time savings on average for distance-based LOD, and ~5.4% and ~5.1% time savings for Morton-based LOD, respectively.

DOI:10.1561/116.00000004