Perceptual Quality Assessment for Point Clouds : A Survey
ZHOU Yingjie, ZHANG Zicheng, SUN Wei, MIN Xiongkuo, ZHAI Guangtao
[Introduction] A point cloud is considered a promising 3D representation that has achieved wide applications in several fields. However, quality degradation inevitably occurs during its acquisition and generation, communication and transmission, and rendering and display. Therefore, how to accurately perceive the visual quality of point clouds is a meaningful topic. In this survey, we first introduce the point cloud to emphasize the importance of point cloud quality assessment (PCQA). A review of subjective PCQA is followed, including common point cloud distortions, subjective experimental setups and subjective databases. Then we review and compare objective PCQA methods in terms of model-based and projection-based. Finally, we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases. This survey provides an overview of classical methods and recent advances in PCQA.
Spatio-Temporal Context-Guided Algorithm for Lossless Point Cloud Geometry Compression
ZHANG Huiran, DONG Zhen, WANG Mingsheng
[Introduction] Point cloud compression is critical to deploy 3D representation of the physical world such as 3D immersive telepresence, autonomous driving, and cultural heritage preservation. However, point cloud data are distributed irregularly and discontinuously in spatial and temporal domain, where redundant unoccupied voxels and weak correlations in 3D space make achieving efficient compression a challenging problem. In this paper, we propose a spatio-temporal context-guided algorithm for lossless geometry point cloud compression. The proposed scheme starts with dividing the point cloud into sliced layers of unit thickness along the longest axis. Then, it introduces a prediction method where both intra-frame and inter-frame point clouds are available, by determining correspondences between adjacent layers and estimating the shortest path using the travelling salesman algorithm. Finally, the few prediction residual is efficiently compressed with optimal context-guided and adaptive fast-mode arithmetic coding techniques. Experiments prove that the proposed method can effectively achieve low bit rate lossless compression of point cloud geometric information, and is suitable for 3D point cloud compression applicable to various types of scenes.