Prediction Enhancement for Point Cloud Attribute Compression Using Smoothing Filter

In recent years, 3D point cloud compression (PCC) has emerged as a prominent research area, attracting widespread attention from both academia and industry. As one of the PCC standards released by the moving picture expert group (MPEG), the geometry-based PCC (G-PCC) adopts two attribute lossy coding schemes, namely the prediction-based Lifting Transform and the region adaptive hierarchical transform (RAHT). Based on statistical analysis, it can be observed that the increase in predictive distance gradually weakens the attribute correlation between points, resulting in larger prediction errors. To address this issue, we propose a prediction enhancement method by using the smoothing filter to improve the attribute coding efficiency, which is both integrated into the Lifting Transform and RAHT. For the former, the neighbor point smoothing method based on the prediction order is proposed via a weighted average strategy. The proposed smoothing is only applied to points in the lower level of details (LoDs) by adjusting the distance-based predicted attribute values. For the latter, we design a neighbor node smoothing method after the inter depth up-sampling (IDUS) prediction, where the sub-nodes in the same unit node are filtered for lower levels. Experimental results have demonstrated that compared with two latest MPEG G-PCC reference software TMC13-v23.0 and GeSTM-v3.0, our proposed enhanced prediction method exhibits superior Bjøntegaard delta bit rate (BDBR) gains with small increase in time complexity.