Set Partitioning in Hierarchical Trees for Point Cloud Attribute Compression

Abstract

We propose an embedded attribute encoding method for point clouds based on set partitioning in hierarchical trees (SPIHT). The encoder is used with the region-adaptive hierarchical transform which has been a popular transform for point cloud coding, even included in the standard geometry-based point cloud coder (G-PCC). The result is an encoder that is efficient, scalable, and embedded. That is, higher compression is achieved by trimming the full bit-stream. G-PCCs RAHT coefficient prediction prevents the straightforward incorporation of SPIHT into G-PCC. However, our results over other RAHT-based coders are promising, improving over the original, nonpredictive RAHT encoder, while providing the key functionality of being embedded.