Cross-Component Residual Prediction for Geometry-Based Point Cloud Compression

Abstract

Point cloud compression is pivotal for the success of immersive multimedia applications. For attribute compression in geometry-based point cloud compression (G-PCC), Region Adaptive Hierarchical Transform (RAHT) is the preferred coding method. Inspired by the significant impact of cross-component prediction in traditional image and video coding, we investigate and present our pioneering work on cross-component residual prediction for RAHT in G-PCC. The method builds on the core observation that cross-component correlations are observed locally in some regions in some sequences. Accordingly, the prediction is employed for last few layers of RAHT which capture local characteristics. We employ a simple linear model, that predicts chroma residues from reconstructed luma residue. The prediction coefficients are learnt on the fly from reconstructed residues of the neighbors. The method gives 1% luma coding gain and around 2-3% chroma coding gain with negligible increase in complexity. The method is adopted to the Geometric Solid Test Model (GeS-TM v7.0), a dedicated codec being developed for solid point clouds.