Point Cloud Encoding and Selective Region Decoding
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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 delivery of services like VR and self-driving applications.
Innovation Solution
A method and device for encoding and decoding point cloud data using bitstreams, incorporating geometry-based and video-based compression techniques, along with 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 quality 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 segmented into multiple bitstreams based on different quality levels or regions of interest. This allows the receiver to selectively decode only the necessary portions of data, reducing overall decoding complexity and time while maintaining high quality where needed.
Solution Approach 2:
The patent employs variable quality parameters across different parts of the point cloud data. By adjusting compression ratios, resolution, or detail levels in different segments, the system optimizes the balance between overall service quality and processing efficiency, reducing latency in less critical areas while maintaining high quality in important regions.
2Reliability
If point cloud data is transmitted with high quality for VR and self-driving services, then service quality is improved, but encoding and decoding complexity increase
Solution Approach 1:
The encoding process divides point cloud data into multiple bitstreams with different complexity levels. The receiver can select appropriate bitstreams based on processing capabilities, reducing decoding complexity while maintaining service quality. This segmentation allows complex processing only where absolutely necessary.
Solution Approach 2:
The system implements differential encoding where full quality and complexity processing is applied only to critical regions or frames, while other areas use reduced complexity encoding. This partial application of high-quality processing reduces overall computational burden while maintaining service quality in essential areas.
Data Source
AI summary
A point cloud data transmission method according to embodiments may comprise the steps of encoding point cloud data, and transmitting the point cloud data. A point cloud data reception device according to embodiments may comprise a reception unit for receiving a bitstream including point cloud data, and a decoder for decoding the point cloud data.