Point Cloud Compression with G-PCC and V-PCC for Low-Latency Delivery
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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 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), to facilitate efficient transmission and rendering of point cloud content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If geometry-based point cloud compression (G-PCC) is used, then encoding precision is improved, but encoding time increases
Solution Approach 1:
The patent divides the point cloud data processing into multiple independent modules including octree construction, occupancy code generation, and arithmetic encoding. Each module processes specific aspects of the data independently, allowing for optimized processing of large point clouds while maintaining high encoding precision through systematic decomposition of the encoding task.
Solution Approach 2:
The patent performs preliminary organization of point cloud data into octree structures and generates occupancy codes before the actual compression encoding. This preliminary structuring prepares the data in advance for efficient arithmetic encoding, reducing the time required during the actual encoding process while maintaining precision.
2Productivity
If video-based point cloud compression (V-PCC) is used, then encoding speed is improved, but encoding precision deteriorates
Solution Approach 1:
The patent merges the advantages of both G-PCC and V-PCC by integrating geometry-based octree structuring with video-based prediction techniques. This combination allows for faster encoding through video prediction methods while maintaining the structural precision of octree-based geometry encoding, thus improving both speed and precision simultaneously.
Solution Approach 2:
The patent employs a composite encoding approach that combines different compression techniques (octree-based geometry encoding and video-based prediction) into a unified framework. This composite method leverages the strengths of each technique to achieve both high encoding speed and high encoding precision that neither method could achieve alone.
3Reliability
If large amounts of point cloud data are transmitted, then service quality is improved, but transmission latency increases
Solution Approach 1:
The patent extracts and transmits only the essential occupancy codes and arithmetic encoding results rather than transmitting all raw point cloud data. This extraction of critical information reduces the transmission data volume significantly, lowering latency while maintaining the quality needed for accurate point cloud reconstruction at the receiver端.
Solution Approach 2:
The patent transforms the point cloud data from its original high-volume format into compressed parameter representations (occupancy codes, arithmetic encoding symbols). This parameter transformation reduces the data size for transmission while preserving the essential geometric information, thereby reducing latency without compromising 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 including the point cloud data. A point cloud data reception method according to embodiments may comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data.