Abstract
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 language | English |
|---|---|
| Pages (from-to) | 1564-1577 |
| Journal | IEEE Transactions on Circuits and Systems for Video Technology |
| Volume | 32 |
| Issue number | 3 |
| Early online date | 30 Mar 2021 |
| DOIs | |
| Publication status | Published - Mar 2022 |
| Externally published | Yes |
Bibliographical note
This article was recommended by Associate Editor Y. Yang.Funding
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0831003, in part by the National Natural Science Foundation of China under Grant 61871342, in part by the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, under Grant VRLAB2019B03, and in part by the Hong Kong RGC under Grant 9042955 (CityU 11202320).
Keywords
- 3D point clouds
- image/video compression
- rate distortion optimization
- sparse representation