Temporal Filtering for Region Adaptive Hierarchical Transform in Geometric Point Cloud Compression.

Dynamic point cloud compression is critical for the success of immersive multimedia applications and autonomous driving. For attribute compression in geometric point cloud compression (G-PCC), Region Adaptive Hierarchical Transform (RAHT) is the preferred coding method. Recently, inter-prediction was introduced for RAHT in G-PCC. In inter-RAHT, the transform coefficients of the top layers (low frequency coefficients) are predicted by a simple copy of the transform coefficients from the reference frame. Such a prediction assumes reference and current RAHT layers are perfectly correlated, which is barely true. To address this, the paper introduces a temporal filtering mechanism for RAHT. Specifically, we scale the reference layer by a filtering coefficient. Different filtering coefficients are designed for different RAHT layers, thereby emulating a low-pass filter. Experiments show an average 2% bit-rate savings over G-PCC test model tmc13-v22 with negligible increase in complexity. The method has been adopted to the second version of G-PCC.