Point Cloud Bitstream Coding with Feedback for Low-Latency Streaming
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data due to latency and encoding/decoding complexity, which hinders high-quality services such as VR and self-driving applications.
Innovation Solution
A method and apparatus for efficiently processing point cloud data through encoding and decoding bitstreams, utilizing geometry-based and video-based point cloud compression coding, and incorporating feedback information to optimize data processing based on user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high fidelity for VR and self-driving services, then service quality is improved, but data transmission latency and processing complexity increase
Solution Approach 1:
The point cloud data is divided into multiple blocks or partitions that can be processed and transmitted independently. This segmentation allows parallel processing of different data portions, reducing overall encoding/decoding complexity and transmission time while maintaining high fidelity through selective prioritization of important blocks.
Solution Approach 2:
The system performs preliminary encoding and prioritization of point cloud data blocks before transmission based on their importance for VR and self-driving applications. Critical data blocks are prepared and transmitted first, reducing latency for time-sensitive operations while maintaining overall service quality.
2Reliability
If comprehensive point cloud data is processed to maintain high service quality, then service reliability is improved, but encoding and decoding complexity increase
Solution Approach 1:
Different encoding precision and compression ratios are applied to different blocks of point cloud data based on their local importance. Regions critical for VR and self-driving services are encoded with higher fidelity, while less critical regions use more aggressive compression, thereby reducing overall complexity while maintaining service quality.
Solution Approach 2:
The system dynamically adjusts encoding parameters such as quantization precision, transformation methods, and compression ratios based on the specific requirements of the point cloud blocks. This adaptive parameter adjustment reduces encoding/decoding complexity for non-critical data while preserving quality where needed.
Data Source
AI summary
A point cloud data transmission method according to embodiments may include the steps of: encoding point cloud data; and transmitting a bitstream including the point cloud data. A point cloud data reception method according to embodiments may include the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data.