Set Partitioning in Hierarchical Trees for Point Cloud Attribute Compression
André Souto, Victor F. Figueiredo, Philip A. Chou, Ricardo Lopes de Queiroz · IEEE Signal Processing Letters · 2021
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.