Point Cloud Compression Switching for Low-Latency Transmission
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data required for applications like virtual reality, augmented reality, and self-driving services due to high latency and encoding/decoding complexity.
Innovation Solution
A method and device for encoding and decoding point cloud data using geometry-based and video-based compression techniques, including geometry-based point cloud compression (G-PCC) and video-based point cloud compression (V-PCC), with components like a point cloud video encoder and decoder, to manage and transmit point cloud data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If geometry-based point cloud compression (G-PCC) is used, then encoding/decoding complexity is reduced, but compression efficiency and quality may be limited
Solution Approach 1:
The patent segments point cloud data processing into multiple representation methods including geometry-based compression (G-PCC) and video-based compression (V-PCC). The system divides the point cloud data into different regions or levels of detail, allowing selective application of compression techniques to balance complexity and efficiency for different data portions.
Solution Approach 2:
The patent implements dynamic switching between different compression modes (geometry-based and video-based) based on content characteristics, latency requirements, and quality needs. The system can adaptively adjust the compression approach for different frames or regions, optimizing the balance between complexity and compression efficiency in real-time.
2Loss of information
If video-based point cloud compression (V-PCC) is used, then compression efficiency is improved, but latency increases
Solution Approach 1:
The patent applies partial video-based compression only to specific regions or frames where high compression efficiency is most beneficial, rather than processing entire point cloud sequences with V-PCC. This selective application reduces overall latency while maintaining compression efficiency where needed.
Solution Approach 2:
The system implements periodic switching between geometry-based and video-based compression modes, using V-PCC at specific intervals or for specific content types while relying on faster G-PCC for other portions, thereby balancing compression efficiency with latency constraints.
3Measurement precision
If high-resolution point cloud data is transmitted, then service quality is improved, but data transmission volume and processing load increase
Solution Approach 1:
The patent applies different compression and transmission quality levels to different regions of the point cloud data based on their importance. Critical regions (e.g., foreground objects, areas of interest) are transmitted with higher precision while less important regions use lower precision, reducing overall data volume while maintaining service quality where it matters most.
Solution Approach 2:
The patent transforms point cloud data into alternative representations (such as voxel grids, meshes, or video sequences) that enable more efficient compression and transmission. By changing the dimensional representation of the data, the system achieves high service quality with reduced transmission volume through more compact data structures.
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data.