.png){kind=icon}
Return
Low Complexity Coding Unit Decision for Video-Based Point Cloud Compression
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2023.3337637.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With growing demand for point cloud coding, Video-based Point Cloud Compression (V-PCC) is released for dynamic point clouds, relying on mature 2D video coding techniques. However, the huge computational complexity of 2D video codec is inherited by V-PCC, thereby resulting in a notably time-consuming encoding process for the projection videos. To accelerate the compression, this paper proposes a low complexity coding unit decision algorithm for V-PCC intra coding. First, the 2D sequences (occupancy, geometry, and attribute sequences) are projected from same 3D point could frames in V-PCC. By exploring the strong correlations among them, the cross-projection information is creatively proposed for improving the predication performance of CU partition. Second, considering the disparate coding losses generated by incorrect partitioning decisions of different CUs, we develop a rate-distortion-oriented learning approach aimed at increasing the decision accuracy of the CUs, severely affecting coding performance. Third, to accommodate the particular coding architecture of V-PCC intra configuration, we further devise an overall framework, including targeted feature extraction and partitioning decision for intra and inter coding of geometry and attribute sequences. The final experimental results strongly demonstrate the effectiveness of our proposed algorithm. The time consumption of total projection sequence compression can be reduced by 57.80%, while the coding losses on Geom.BD-TotalRate (D1 and D2) and Attr.BD-TotalRate Luma component are only 0.08%, 0.33%, and 0.16%, respectively, which can be negligible. To the best of our knowledge, the proposed algorithm achieves state-of-the-art performance for accelerating the projection sequence compression in V-PCC All-Intra configuration.
Keywords:
V-PCC
CU partition
machine learning
cross-projection information
rate-distortion-oriented
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSaveShareSaveShareSave View more