Adaptive LPU Decision for Dynamic Point Cloud Compression

With the rapid development of point cloud applications, dynamic point cloud compression has become a hot topic. A fast and accurate motion estimation scheme is the focus of dynamic point cloud compression, and the size of prediction units (PUs) affects the result of motion estimation. Improper size may even make the performance of inter-frame coding worse than that of intra-frame coding. However, the size of PUs is not fully explored. In this letter, we explore the impact of PUs size on inter-frame coding and propose an adaptive largest prediction unit (LPU) decision strategy. We first downsample original point clouds and obtain the features of adjacent frames. Then, the relationship between the optimal size of LPU and the features of adjacent frames is built. Finally, the optimal size of LPU is used to guide the inter-frame coding. Experimental results show that the better and faster coding performance is achieved by our algorithm, where the bitrate is saved by 2.48%, and the encoding time is saved by 33.80% for point cloud lossless geometry compression. Moreover, our method ensures that inter-frame coding performance of G-PCC is superior to intra-frame coding in all the sequences.