A Hybrid Compression Framework for Color Attributes of Static 3D Point Clouds

Hao LIU, Hui YUAN, Qi LIU, Junhui HOU, Huanqiang ZENG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

55 Citations (Scopus)


The emergence of 3D point clouds (3DPCs) is promoting the rapid development of immersive communication, autonomous driving, and so on. Due to the huge data volume, the compression of 3DPCs is becoming more and more attractive. We propose a novel and efficient color attribute compression method for static 3DPCs. First, a 3DPC is partitioned into several sub-point clouds by color distribution analysis. Each sub-point cloud is then decomposed into a lot of 3D blocks by an improved k-d tree-based decomposition algorithm. Afterwards, a novel virtual adaptive sampling-based sparse representation strategy is proposed for each 3D block to remove the redundancy among points, in which the bases of the graph transform (GT) and the discrete cosine transform (DCT) are used as candidates of the complete dictionary. Experimental results over 10 common 3DPCs demonstrate that the proposed method can achieve superior or comparable coding performance when compared with the current state-of-the-art methods.
Original languageEnglish
Pages (from-to)1564-1577
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number3
Early online date30 Mar 2021
Publication statusPublished - Mar 2022
Externally publishedYes


  • 3D point clouds
  • image/video compression
  • rate distortion optimization
  • sparse representation


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