Attribute compression of 3D point clouds using Laplacian sparsity optimized graph transform

@article{Shao2017AttributeCO,
  title={Attribute compression of 3D point clouds using Laplacian sparsity optimized graph transform},
  author={Yiting Shao and Zhaobin Zhang and Zhu Li and Kui Fan and Ge Li},
  journal={2017 IEEE Visual Communications and Image Processing (VCIP)},
  year={2017},
  pages={1-4}
}
3D sensing and content capturing have made significant progress in recent years and the MPEG standardization organization is launching a new project on immersive media with point cloud compression (PCC) as one key corner stone. In this work, we introduce a new binary tree based point cloud partition and explore the graph signal processing tools, especially the graph transform with optimized Laplacian sparsity, to achieve better energy compaction and compression efficiency. The resulting rate… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-4 OF 4 CITATIONS

Point Clouds Attribute Compression Using Data-Adaptive Intra prediction

  • 2018 IEEE Visual Communications and Image Processing (VCIP)
  • 2018
VIEW 5 EXCERPTS
CITES METHODS

Rate-Distortion Driven Adaptive Partitioning for Octree-Based Point Cloud Geometry Coding

  • 2018 25th IEEE International Conference on Image Processing (ICIP)
  • 2018
VIEW 1 EXCERPT
CITES BACKGROUND

References

Publications referenced by this paper.