Hybrid Point Cloud Attribute Compression Using Slice-based Layered Structure and Block-based Intra Prediction

Point cloud compression is a key enabler for the emerging applications of immersive visual communication, autonomous driving and smart cities, etc. In this paper, we propose a hybrid point cloud attribute compression scheme built on an original layered data structure. First, a slice partition scheme and a geometry-adaptive k-dimensional tree (k-d tree) method are devised to generate layer structures. Second, we introduce an efficient block-based intra prediction scheme containing to exploit spatial correlations among adjacent points. Third, an adaptive transform scheme based on Graph Fourier Transform (GFT) is Lagrangian optimized to achieve better transform efficiency. The Lagrange multiplier is off-line derived based on the statistics of attribute coding. Last but not least, multiple scan modes are dedicated to improve coding efficiency for entropy coding. Experimental results demonstrate that our method performs better than the state-of-the-art region-adaptive hierarchical transform (RAHT) system, and on average a 37.21% BD-rate gain is achieved. Comparing with the test model for category 1 (TMC1) anchors, which were recently published by MPEG-3DG group on 121st MPEG meeting, a 8.81% BD-rate gain is obtained.