Embedded Coding of Point Cloud Attributes

Point cloud compression (PCC) has been rapidly evolving in the context of international standards. Despite the inherent scalability of octree-based geometry descriptions, current attribute compression techniques prevent full scalability of compressed point clouds. We propose an improvement on an embedded attribute encoding method for point clouds based on set partitioning in hierarchical trees (SPIHT). We propose to use a multi-layer perceptron (MLP) to model contexts in order to further compress the final bit-stream. The encoder is used along with the region-adaptive hierarchical transform, which has been a popular transform for point cloud coding and is included in the standard geometry-based point cloud coder (G-PCC). The result is an encoder that is efficient, scalable, and, best of all, embedded. That is, higher compression is achieved by further trimming the single bit-stream. Experimental results show approximately 13% BD-Rate reduction using MLP-based context modeling.