Motion Estimation Techniques for Volumetric Video Attribute Compression
For point cloud compression researchers, this work addresses the underexplored area of motion-based attribute compression, providing significant bitrate reductions over existing standards.
The paper proposes a geometry-based inter-coding scheme with graph-based motion estimation and interpolation-free fractional-voxel motion estimation for attribute compression of dynamic point clouds, achieving average bitrate savings of 55.3%, 42.3%, and 16.5% over G-PCC, GeS-TM, and V-PCC under lossy-geometry conditions.
Point cloud compression relies on techniques to compress both geometry and attributes. Motion-based approaches for dynamic solid point cloud geometry compression within the geometry-based point cloud compression (G-PCC) framework have achieved significant reductions in geometry rate. However, motion-based techniques for attribute compression remain underexplored, making it challenging to achieve significant reductions in the temporal redundancy of attributes. Firstly, this paper proposes a geometry-based inter-coding scheme to compress the attributes of dynamic solid point clouds. Secondly, a graph-based motion-estimation scheme for point-cloud attribute compression is proposed. Thirdly, an interpolation-free fractional-voxel motion estimation method is proposed to refine motion accuracy to fractional-voxel precision. Our experimental results on the MPEG point cloud dataset show that the proposed scheme outperforms G-PCC, GeS-TM, and V-PCC in lossless and lossy geometry conditions. We achieve average bitrate savings of $55.3\%$, $42.3\%$, and $16.5\%$ over G-PCC, GeS-TM, and V-PCC, respectively, under lossy-geometry conditions.