Point Cloud Compression Based on Joint Optimization of Graph Transform and Entropy Coding for Efficient Data Broadcasting

The worldwide commercialization of fifth generation (5G) wireless networks are pushing toward the deployment of immersive and high-quality VR-based telepresence systems. Among them, 3D object is generally digitized and represented as point cloud. However, realistically reconstructed 3D point clouds generally contain thousands up to millions of points, which brings a huge amount of data. Therefore, efficient compression of point cloud is an essential part to enable emerging immersive 3D visual communication. In point cloud compression, the graph transform is an effective tool to compact the energy of color signals on the voxels in the 3D space. However, as the eigenbasis of the graph transform is obtained from the graph Laplacian of the constructed graph, the corresponding eigenvalues will be related to the probability distributions of the transformed coefficients, which finally affect the coding efficiency of entropy coding for the quantized coefficients. To overcome the interdependence between graph transform and entropy coding, this paper proposes a jointly optimized graph transform and entropy coding scheme for compressing point clouds. Firstly, we modify the traditional graph Laplacian constructed on the geometry of the point clouds by multiplying a color signal-related matrix. Secondly, we theoretically devise the expected rate and distortion induced by quantization on the graph transformed coefficients. Finally, we propose a Lagrangian multiplier based algorithm to derive the optimum scaling matrix given a quantization parameter. Experimental results are presented to demonstrate that the proposed joint graph transform and entropy coding scheme can significantly outperform its transform coding based counterparts in compressing the color attribute of point clouds.