Advances in Predictive RAHT for Geometric Point Cloud Compression.

Abstract

Point cloud compression is critical for the success of immersive multimedia applications. For attribute compression in geometric point cloud compression (G-PCC), Region Adaptive Hierarchical Transform (RAHT) is the preferred coding method. This paper presents several advances to predictive coding with RAHT: 1) Sample Domain Prediction: Prediction in RAHT is done in transform domain. This introduces undesirable distortion to the prediction signal because of fixed-point computations and leads to increased decoding complexity. We address this by naturally applying prediction in sample domain. The method opens door to skip the transform stage altogether when all residues are quantized to zero, leading to a significantly light decoder. 2) Reference Node Resampling: Inter-prediction signal derived in RAHT could have a different occupancy and weight distribution compared to the current block, causing a mismatch. To address this, we resample the reference node and align the occupancy and weight distribution. 3) Temporal Filtering: During inter-prediction, the reference node is simply copied as the prediction signal. This assumes a correlation coefficient of unity, which is barely true. We introduce a temporal filtering mechanism conditioned on the sub-band, that emulates a low-pass filtering and achieves improved prediction. 4) Inter-Eligibility: During AC inter-prediction, both encoder and decoder have access to the DC of the current and the reference nodes. We use this information to derive an inter-eligibility criterion. Experimental results show considerable gains and reduced complexity that demonstrate the utility of the proposed methods. All the presented methods have been adopted to the second version of G-PCC.