Fine-Grained Correlation Representation for Graph-Based Point Cloud Attribute Compression

Fei Song, Ge Li, Xiaodong Yang, Wei Gao, Thomas H. Li

Abstract: Recent years have witnessed remarkable success of Graph Fourier Transform (GFT) in point cloud attribute compression. A key to good compression performance of GFT is to construct the graph Laplacian matrix that accurately models signal correlation. Nevertheless, existing attribute compression methods based on GFT adopt the distance metric to define the Laplacian matrix, which does not well represent the color correlation in case of a poor relationship between geometry and color. Hence, considering point cloud color variation in space, we propose additional three kinds of fine-grained correlation representation as Laplacian matrices for patches with different texture categories. Furthermore, we utilize texture complexity features as prior to design a stage-wise decision strategy for guiding each patch to choose appropriate correlation representation in low computation complexity. Experimental results demonstrate our method achieves better compression performance compared with other platforms. Meanwhile, additional experiments also adopt Lagrangian Rate-Distortion Optimization (RDO) to choose optimal one from three correlation representations, verifying the effectiveness of our proposed stage-wise decision strategy.