Rate Allocation for Unequal Error Protection in Dynamic Point Cloud Owing to the characteristics of exquisite and efficient presentation, 3D point cloud has attracted significant attentions. Given that dynamic point cloud consists of geometry and texture information, and they often possess different importance on the visual quality. In this paper, we propose a rate allocation scheme for Unequal Error Protection (UEP) in dynamic point cloud. The proposed algorithm is based on the Videobased Point Cloud Compression (VPCC) standard, which processes point cloud into 2D frame sequences. We allocate the code rate according to different contributions of geometry and texture content to the visual quality, as well as the frame information. The objective evaluation result shows that the proposed UEP algorithm offers quality improvement compared with the Equal Error Protection (EEP) scheme, which is also confirmed by the subjective quality assessment.

Enhanced Intra Prediction Scheme in Point Cloud Attribute Compression 3D point cloud compression (PCC) has been an attractive field with increasing applications in recent years. Moving Picture Experts Group (MPEG) is building an open standard for point cloud compression, consisting of two solutions, videobased point cloud compression (VPCC) and geometrybased point cloud compression (GPCC). As an essential process in GPCC, Knearest neighbors (KNN) algorithm is adopted to perform intra prediction, which only considering distancebased local similarity but neglecting the overall geometric distribution of the neighbor set. In this paper, we propose an enhanced intra prediction scheme based on pointcloud geometric distribution. The centroidbased criterion is introduced to measure the uniformity of spatial distribution of predictive reference points. Our scheme is implemented in GPCC reference software. Experimental results demonstrate that our proposed method can optimize the selection of predictors, which leads to better ratedistortion (RD) performance than the GPCC anchor on point cloud datasets under the common test conditions (CTC).