Quantization Parameter Cascading for Lossy Point Cloud Attribute Compression in G-PCC

Region adaptive hierarchical transform (RAHT) is employed in G-PCC to make attribute compression more efficient. The performance of RAHT is closely related to the quantization parameter (QP), where applying different QPs to different transform depths is beneficial for coding efficiency. In this paper, QP cascading (QPC) is designed based on rate-distortion modelling. Firstly, the single-layer rate-quantization and distortion-quantization models are built by investigating the distribution of residuals. Later, the dependency of adjacent layers is studied to establish the rate-distortion model with dependency. Based on the proposed model, a rate-distortion optimization (RDO) guided QPC (O-QPC) and a fast implementation (F-QPC) are proposed. The experimental results verify the efficiency of the proposed methods. Compared with the G-PCC anchor, under the lossless geometry compression, O-QPC achieves an average of 1.5% performance gain in luma and nearly 13% gain in chroma, and F-QPC achieved an average performance gain of 1.0% in luma and almost 11% in chroma; Under the lossy geometry compression, O-QPC obtained an average of 3.9% gain in luma, and 13% gain in chroma, and F-QPC achieved an average of 3.4% gain in luma and nearly 12% gain in chroma. In particular, F-QPC achieves gains with almost no increase in complexity.