LiDAR point cloud plays an important role in various real-world areas. It is usually generated as sequences by LiDAR on moving vehicles. Regarding the large data size of LiDAR point clouds, Dynamic Point Cloud Compression (DPCC) methods are developed to reduce transmission and storage data costs. However, most existing DPCC methods neglect the intrinsic misalignment in LiDAR point cloud sequences, limiting the rate–distortion (RD) performance. This paper proposes a Misalignment-aware Dynamic LiDAR Point Cloud Compression method (MDLPCC), which alleviates the misalignment problem in both macroscope and microscope. MDLPCC exploits a global transformation (GlobTrans) method to eliminate the macroscopic misalignment problem, which is the obvious gap between two continuous point cloud frames. MDLPCC also uses a spatial–temporal mixed structure to alleviate the microscopic misalignment, which still exists in the detailed parts of two point clouds after GlobTrans. The experiments on our MDLPCC show superior performance over existing point cloud compression methods.

In recent years, the task of learned point cloud compression has gained prominence. An important type of point cloud, LiDAR point cloud, is generated by spinning LiDAR on vehicles. This process results in numerous circular shapes and azimuthal angle invariance features within the point clouds. However, these two features have been largely overlooked by previous methodologies. In this paper, we introduce a model-agnostic method called Spherical-Coordinate-based learned Point cloud compression (SCP), designed to fully leverage the features of circular shapes and azimuthal angle invariance. Additionally, we propose a multi-level Octree for SCP to mitigate the reconstruction error for distant areas within the Spherical-coordinate-based Octree. SCP exhibits excellent universality, making it applicable to various learned point cloud compression techniques. Experimental results demonstrate that SCP surpasses previous state-of-the-art methods by up to 29.14% in point-to-point PSNR BD-Rate.

Developing an efficient quality control method for streaming a 3D space expressed by point clouds is mandatory. Based on this motivation, we develop a Level of Detail (LOD)-based quality optimization method for 3D space point cloud streaming. The proposed method adopts a 3D tile-based method and considers a distance between a user's viewpoint and a 3D tile (i.e., LOD) to maximize the user's perceptual quality. In the experiment, we capture an actual 3D space of our laboratory room using a LiDAR camera and conduct performance evaluations on the Unity platform. The results conclude that the proposed method achieves the highest perceptual quality.

Triangle soup (Trisoup) is being studied as a new coding tool for Geometry-based Point Cloud Compression (G-PCC) stan-dardized in the Moving Picture Experts Group (MPEG). Outside of MPEG, a variable-node-size extension of Trisoup is studied to increase the flexibility of G-PCC. A primary advantage of variable node size is to achieve better coding performance by selecting appropriate node size according to local geometric complexity and required bits. However, the node size is not optimized in terms of bit rate and distortion in the conventional extension. To maximize the coding performances of the variable-node-size method, we propose a new cost function considering both bit rates and distortions. The experimental results show that the proposed method provides -1.5 % coding performance improvement in point-to-point PSNR versus bit rate against the conventional extension.

We previously proposed a novel lossless coding method that utilizes example search and adaptive prediction within a framework of probability model optimization for monochrome video. In this paper, we improve the adaptive prediction in terms of coding performance and processing time. More precisely, we made modifications to the following three items: (a) reference pel arrangements, (b) motion vector derivation, and (c) optimal selection of predictors. Experimental results show that the proposed method certainly improves the coding performance and the processing time compared to our previous method, and achieves better coding performance than the VVC-based lossless video coding scheme.

Overlapped block motion compensation (OBMC) is one of the inter prediction tools that improves coding performance. OBMC applied to various non-squared blocks has been studied in VVC, which is being standardized by joint video experts team (JVET), to improve coding performance over HEVC. Memory bandwidth, however, is a bottleneck when OBMC is used, and conventional methods have not achieved a good trade-off regarding coding performance and memory bandwidth so far. In this study, interpolation filters and applicable conditions of OBMC depending on block sizes are proposed to achieve the best trade-off. The experimental results show a -0.40% BD-rate gain compared with that of the VVC test model 3 for random access conditions under the common test condition in JVET.

We previously proposed a novel lossless coding method that utilizes example search and adaptive prediction within a framework of probability model optimization for still images. Additionally, we also proposed a lossless video coding method where the example search is performed on not only the current but also the previous frames to exploit intra- and inter-frame correlations. In this paper, we integrate these two methods for efficient lossless video coding. Moreover, we extend the adaptive prediction to exploit both spatial and temporal correlations simultaneously. In other words, reference pels used for the prediction are taken from both the current and the motion-compensated previous frames, and their weights, i.e. prediction coefficients, are trained pel-by-pel in a weighted least square manner. The experimental results show that the proposed method achieves better coding performance than the VVC-based lossless video coding scheme.

Intra interpolation filters for intra angular prediction play an important role in the coding performance. In the intra angular prediction of VVC, which is being standardized by the joint video coding expert team (JVET), block-size based switchable interpolation filters between 4-tap cubic and Gaussian interpolation filters is being studied. Although the two filters have different frequency characteristics, block size-based criteria are insufficient to represent the reference sample characteristics. In this manuscript, switching criteria based on both the block-size and QP value are proposed to improve the coding performance. The experimental results show a -0.45% BD-rate gain compared with that by the VVC test model 2 for all intra conditions under the common test condition (CTC) in JVET.