VQBA: Visual-Quality-Driven Bit Allocation for Low-Latency Point Cloud Streaming

Video-based Point Cloud Compression (V-PCC) is an emerging standard for encoding dynamic point cloud data. With V-PCC, point cloud data is segmented, projected, and packed on to 2D video frames, which can be compressed using existing video coding standards such as H.264, H.265 and AV1. This makes it possible to support point cloud streaming via reliable video transmission systems. On the other hand, despite recent advances, many issues still remain and prevent V-PCC from being used in low-latency point cloud streaming. For instance, point cloud registration and patch generation can take a long time. In this paper, we focus on one unique problem in V-PCC: bit allocation among different sub-streams - the geometry sub-stream and the attribute (color) sub-stream - with the goal of improving the visual quality of point clouds under the target bitrate. Existing approaches either do not fully utilize the available bandwidth or can take a long time to run, which cannot be used in scenarios that require low-latency. To this end, we propose a lightweight, frequency-domain-based profiling method for transforming the dynamic point cloud data into a one-dimension vector. By using two single-layer linear regression models, we can estimate the compressed bitrate for geometry data and color information. This allows us to perform bit allocation between the geometry map and the attribute map with simple calculations. Evaluation results show that compared to the baseline approach, our method can achieve better visual qualities with smaller encoded segment sizes under the target bitrate.