Cluster-based two-branch framework for point cloud attribute compression

Owing to the irregular distribution of point clouds in 3D space, effectively compressing the point cloud is still challenging. Recently, numerous compression methods have been developed with outstanding performance for the compression of geometry information of point clouds. On the contrary, limited explorations have been devoted to point cloud attribute compression (PCAC). Thus, this paper focuses on the study of point cloud attribute compression by applying geometric information as decoded prior information. In this paper, a novel cluster-based two-branch framework for PCAC is proposed. Specifically, the point cloud is first divided into adaptive un-overlapped blocks via K-means according to geometric information, which enables an efficient local representation of the point cloud. Then, we split the point cloud attributes into two components(Block-Mean component and Block-Residual component) and compressed them separately through the designed block-based dual branches. For the Block-Mean component, we design a prediction scheme to remove the inter-block attribute redundancy. The Block-Residual component is further compacted by applying a designed graph Fourier transform generated by geometric prior information. Then, the transform coefficients are encoded using an arithmetic coder. Extensive experimental results demonstrate that the proposed point cloud attribute compression scheme outperforms existing attribute compression schemes in terms of both objective quality and subjective quality.