A Quality-Aware Sampling Framework for Efficient 3D Point Cloud Transmission

The large volume of data from the point cloud brings significant demands on network bandwidth. However, the current transmission framework only considers using lossy compression to control the size of data, while ignoring visually redundant information due to the setting of rendering devices. Based on the fact that point overlapping might occur for the case that a dense point cloud is rendered on a relatively low resolution 2D monitor, we propose a novel quality-aware sampling framework for point cloud transmission. When a target visual quality is determined, an optimal sampling module is designed to remove overlapped points with the help of a simple but effective quality model. By taking into account the impact of multiple factors (i.e., sampling, lossy compression, and client rendering resolution), this quality model can predict the final perceptual quality in the client. Based on a newly constructed dataset which consists of 420 samples, experiment results show that the proposed transmission framework can significantly reduce bandwidth cost (e.g., 6.10% to 84.43%) and processing time (e.g., 8.99% to 92.53%) without introducing noticeable distortion under certain rendering conditions, thus achieving higher bandwidth utilization and better real-time performance.