Occupancy-map-based rate distortion optimization for video-based point cloud compression
This work addresses inefficiencies in point cloud compression for applications like virtual reality, but it is incremental as it builds on existing video-based methods.
The paper tackles the problem of wasted bits in video-based point cloud compression by optimizing rate distortion for unoccupied pixels, achieving average bitrate savings of 11.9% for geometry and 15.4% for attribute.
The state-of-the-art video-based point cloud compression scheme projects the 3D point cloud to 2D patch by patch and organizes the patches into frames to compress them using the efficient video compression scheme. Such a scheme shows a good trade-off between the number of points projected and the video continuity to utilize the video compression scheme. However, some unoccupied pixels between different patches are compressed using almost the same quality with the occupied pixels, which will lead to the waste of lots of bits since the unoccupied pixels are useless for the reconstructed point cloud. In this paper, we propose to consider only the rate instead of the rate distortion cost for the unoccupied pixels during the rate distortion optimization process. The proposed scheme can be applied to both the geometry and attribute frames. The experimental results show that the proposed algorithm can achieve an average of 11.9% and 15.4% bitrate savings for the geometry and attribute, respectively.