Point Cloud Compression with Tiled Encoding 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 device for encoding and decoding point cloud data using geometry-based and video-based compression techniques, including geometry and attribute encoding, with feedback information integration to optimize data processing based on user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If point cloud data is transmitted with high quality for VR and self-driving services, then service quality is improved, but data transmission latency and encoding/decoding complexity increase
Solution Approach 1:
The point cloud data is divided into multiple tiles or partitions, allowing parallel processing and transmission of different segments. This segmentation enables the system to process and transmit data in smaller manageable units, reducing overall encoding/decoding complexity and transmission time while maintaining high quality for VR and self-driving applications
Solution Approach 2:
The patent performs preliminary encoding and preparation of point cloud data before actual transmission. By pre-processing the data into optimized formats and structures, the system reduces the computational burden during real-time transmission and decoding, thereby lowering latency while preserving service quality
2Reliability
If point cloud data is transmitted with high quality for VR and self-driving services, then service quality is improved, but encoding/decoding complexity increases
Solution Approach 1:
The point cloud data is divided into multiple tiles or partitions, allowing parallel processing and transmission of different segments. This segmentation enables the system to process and transmit data in smaller manageable units, reducing overall encoding/decoding complexity and transmission time while maintaining high quality for VR and self-driving applications
Solution Approach 2:
The patent applies different encoding complexities to different regions of the point cloud data based on their importance. Critical regions for self-driving and VR applications receive higher quality encoding, while less critical areas use simpler encoding, thereby reducing overall computational complexity while maintaining necessary service quality
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.