Set Partitioning in Hierarchical Trees for Point Cloud Attribute Compression
preprintposted on 26.07.2021, 04:03 by Ricardo de QueirozRicardo de Queiroz, Andre Souto, Victor Figueiredo, Philip Chou
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-PCC’s 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.