Point Clouds Attribute Compression Using Data-Adaptive Intra prediction

In recent years, 3D sensing and capture technologies have made constant progress, leading to point clouds with higher resolution and fidelity. Since most applications demand compact storage and fast transmission, the issue of how to compress point clouds efficiently becomes an intractable problem. While previous GFT-based solutions use the transform tool to decorrelate attributes directly, ignoring the overall attribute's data spatial redundancy, Graph Fourier Transform (GFT) has shown good performance on point cloud attribute compression. So, motivated by coding tools in traditional image and video coding, we propose a block-based data-adaptive intra prediction tool before graph transform processing to further reduce the redundancy. We adopt uniform quantizing and context-based arithmetic coding to get the final bitstream. Experimental results on different datasets demonstrate that our method improves the compression efficiency of other GFT-based schemes and has much better BD-BR performance than the state-of-the-art Region-Adaptive Hierarchical Transform (RAHT) approach on most specified point cloud contents.