Sample Domain Prediction and Transform Skip for Region Adaptive Hierarchical Transform in Geometric Point Cloud Compression

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. Although RAHT was initially introduced as a pure transform coding tool, recent advancements have introduced intra and inter prediction for RAHT. However, these methods perform prediction in transform domain which is sub-optimal since: ${i}$) fixed-point RAHT introduces distortion to the prediction signal and $i {i}$) transforming prediction signal leads to additional decoding complexity. To address this, we propose to perform prediction in sample domain, thereby retaining crisp prediction signal and alleviating decoder of unnecessary computations. Performing prediction in sample domain opens door to completely skip the transform stage at the decoder when all the residue of a block are quantized to zero, leading to further complexity reduction. The proposed methods achieve an average chroma coding gain of around $1 \%$ and reduces the overall decoding complexity by $3-5 \%$. The method is adopted to the next version of Geometric Solid Test Model (GeS-TM v5.0) and is being evaluated on G-PCC test model TMC13v25.