Gu Shuai, Hou Junhui, Zeng Huanqiang, Yuan Hui, Ma Kai-Kuang
IEEE Trans Image Process. 2019 Aug 27. doi: 10.1109/TIP.2019.2936738.
3D point clouds associated with attributes are considered as a promising paradigm for immersive communication. However, the corresponding compression schemes for this media are still in the infant stage. Moreover, in contrast to conventional image/video compression, it is a more challenging task to compress 3D point cloud data, arising from the irregular structure. In this paper, we propose a novel and effective compression scheme for the attributes of voxelized 3D point clouds. In the first stage, an input voxelized 3D point cloud is divided into blocks of equal size. Then, to deal with the irregular structure of 3D point clouds, a geometry-guided sparse representation (GSR) is proposed to eliminate the redundancy within each block, which is formulated as an ℓ0-norm regularized optimization problem. Also, an inter-block prediction scheme is applied to remove the redundancy between blocks. Finally, by quantitatively analyzing the characteristics of the resulting transform coefficients by GSR, an effective entropy coding strategy that is tailored to our GSR is developed to generate the bitstream. Experimental results over various benchmark datasets show that the proposed compression scheme is able to achieve better rate-distortion performance and visual quality, compared with state-of-the-art methods.
与属性相关联的三维点云被视为沉浸式通信的一种有前景的范例。然而，针对这种媒体的相应压缩方案仍处于起步阶段。此外，与传统图像/视频压缩相比，由于三维点云数据结构不规则，对其进行压缩是一项更具挑战性的任务。在本文中，我们针对体素化三维点云的属性提出了一种新颖且有效的压缩方案。在第一阶段，将输入的体素化三维点云划分为大小相等的块。然后，为了处理三维点云的不规则结构，提出了一种几何引导的稀疏表示（GSR）来消除每个块内的冗余，这被表述为一个ℓ0范数正则化优化问题。此外，应用块间预测方案来去除块之间的冗余。最后，通过定量分析由GSR得到的变换系数的特征，开发了一种针对我们的GSR量身定制的有效熵编码策略来生成比特流。在各种基准数据集上的实验结果表明，与现有方法相比，所提出的压缩方案能够实现更好的率失真性能和视觉质量。
