Content-Aware Rate Control for Geometry-Based Point Cloud Compression

Junteng Zhang, Junzhe Zhang, Wenxi Ma, Dandan Ding, Zhan Ma

Abstract: The Geometry-based Point Cloud Compression (G-PCC) standard enables point cloud delivery over the internet through efficient compression. Limited by the transmission bandwidth, rate control is demanded in G-PCC for high-quality point cloud video streaming. This paper thus proposes a content-aware rate control solution for G-PCC. Given the target bitrate and distortion evaluation criteria, our method can predict the geometry and attribute quantizers for G-PCC while minimizing the overall distortion. Specifically, as the rate and distortion of both geometry and attribute are involved in G-PCC, we separately establish rate/distortion models for geometry and attribute. Moreover, recognizing the dependence of attribute compression on reconstructed geometry, we integrate the geometry quantizer into the attribute rate/distortion models to improve prediction accuracy. For dynamic coding scenarios, we leverage selective representative frames for efficient model parameter initialization. Additionally, we introduce a $\mu $ updating strategy that dynamically incorporates information from previous frames to update the existing models. Extensive experiments demonstrate the effectiveness of our proposed method. Under the G-PCC common test condition, our method achieves remarkable rate accuracy, with a 5.3% bitrate error for static coding and 0.3% for dynamic coding. Moreover, it achieves >15% BD-Rate gains over the G-PCC anchor. These results showcase its capabilities in delivering high-fidelity point cloud video streams within the bandwidth constraint.

External IDs:dblp:journals/tcsv/ZhangZMDM24