Weighted Average Prediction for Region Adaptive Hierarchical Transform in Solid Geometry Point Cloud Compression

Point cloud attribute compression is critical in real-time application scenarios of immersive multimedia data. Within the geometry based point cloud compression (G-PCC) standard, the region-adaptive hierarchical transform (RAHT) based methodology is employed to perform attribute compression with intra or inter prediction. However, current inter prediction or intra prediction cannot provide optimal performance for all RAHT nodes. To further improve prediction efficiency, this paper proposes a weighted average prediction for RAHT based attribute compression. The proposed strategy includes using the weighted average of inter prediction and intra prediction as the additional candidate prediction value, selecting different candidate prediction lists according to the octree depth to determine the final prediction value. We also propose using the prediction information of nodes in the parent layer or the same layer to adaptively derive the weights in average prediction for each node. Experiments on top of Ges-TM v4.0 demonstrate the superior performance of our method. The average coding gain on attribute BD-Rate is -3.2% (Luma), -4.5% (Chroma Cb) and -4.8% (Chroma Cr). The proposed scheme has been adopted to the Solid G-PCC standard.