A efficient predictive wavelet transform for LiDAR point cloud attribute compression

In this paper, a new predictive wavelet transform (PWT) is proposed to solve LiDAR point clouds attribute compression. Our method is a combination of predictive coding and Haar wavelet transform. Based on the spatial information, a hierarchical predictive transform tree is designed to represent 3D irregular data points efficiently. Each level node is classified as a predictive node (P-node) or a transform node (T-node) according to the distances to its adjacent nodes. Then in a top-down coding process, the Haar transform is applied to all T-node pairs, and predictive coding is processed on all P-nodes alternately. It is shown by experimental results that the proposed PWT method offers better R-D performances compared with state-of-the-art methods.