Point clouds are representations of three-dimensional (3D) objects in the form of a sample of points on their surface. Point clouds can be used in real-time 3D immersive telepresence, automotive and robotic navigation, as well as medical imaging. Compared to traditional video technology, point cloud systems allow free viewpoint rendering, as well as mixing of natural and synthetic objects. However, this improved user experience comes at the cost of increased storage and bandwidth requirements as point clouds are typically represented by the geometry and colour (texture) of millions of 3D points. For this reason, major efforts are being made to develop efficient point cloud compression schemes. To standardize point cloud compression (PCC) technologies, the Moving Picture Experts Group (MPEG) launched a call for proposals in 2017. As a result, three point cloud compression technologies were developed: surface point cloud compression (S-PCC) for static point cloud data, video-based point cloud compression (V-PCC) for dynamic content, and LIDAR point cloud compression (L-PCC) for dynamically acquired point clouds. Later, L-PCC and S-PCC were merged under the name geometry-based point cloud compression (G-PCC). In V-PCC, the input point cloud is first decomposed into a set of patches, which are independently mapped to a two-dimensional grid of uniform blocks. This mapping is then used to store the geometry and colour information as one geometry video and one colour video. Next, the generated geometry video and colour video are compressed with a video coder, e.g. H.265/HEVC. Finally, the geometry and colour videos, together with metadata (occupancy map for the two-dimensional grid, auxiliary patch, and block information) are multiplexed to generate the bit stream. In the video coding step, compression is achieved with quantization, which is determined by a quantization step or, equivalently, a quantization parameter (QP). The aim of the OPT-PCC project is to develop algorithms that optimise the rate-distortion performance of V-PCC, i.e. algorithms that minimize the reconstruction error (distortion) for a given bit budget, or, equivalently, minimize the bitrate for the same reconstruction error.
The scientific and training objectives of the project are as follows.
1. O1: build analytical models that accurately describe the effect of the geometry and colour quantization of a point cloud on the bitrate and distortion;
2. O2: use O1 to develop fast search algorithms that optimise the allocation of the available bit budget between the geometry information and colour information;
3. O3: implement a compression scheme for dynamic point clouds that exploits O2 to outperform the state-of-the-art in terms of rate-distortion performance. The target is to reduce the bitrate by at least 20% for the same reconstruction quality;
4. O4: provide multi-disciplinary training to the researcher in algorithm design, metaheuristic optimisation, computer graphics, media production, and leadership and management skills.
