Hybrid Point Cloud Compression for Latency and Encoding Complexity
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data required for virtual reality (VR), augmented reality (AR), mixed reality (MR), 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), with encoding and decoding processes optimized for efficient transmission and reception 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 complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the point cloud data into multiple regions or blocks, processing each segment separately with appropriate encoding methods. This reduces the overall encoding complexity while maintaining precision in critical areas by focusing computational resources on specific segments rather than processing all data uniformly.
Solution Approach 2:
The patent employs dynamic encoding strategies that adapt the encoding precision and complexity based on the characteristics of different point cloud regions. By dynamically adjusting encoding parameters according to local geometry complexity and importance, the system achieves high encoding precision where needed while reducing complexity in less critical areas.
2Speed
If video-based point cloud compression (V-PCC) is used, then processing speed is improved, but encoding precision deteriorates
Solution Approach 1:
The patent merges G-PCC and V-PCC methods into a hybrid encoding framework. This combination leverages the high processing speed of V-PCC while incorporating the high precision advantages of G-PCC for critical geometric details, thus achieving both fast processing and high encoding precision simultaneously.
Solution Approach 2:
The patent applies local quality by using video-based compression for general regions requiring fast processing, while selectively applying geometry-based compression to specific regions where high precision is critical. This localized approach ensures processing speed is maintained overall while precision is improved where necessary.
3Reliability
If large amounts of point cloud data are transmitted, then service quality is improved, but transmission latency increases
Solution Approach 1:
The patent segments the large point cloud data into multiple smaller packets or layers, enabling progressive transmission where critical data is sent first. This allows the receiving system to begin processing and displaying content before all data is received, significantly reducing perceived latency while maintaining service quality.
Solution Approach 2:
The patent applies preliminary action by performing compression and prioritization of data packets before transmission. Critical geometric information and high-priority data are prepared and positioned for first transmission, allowing the system to deliver high-quality service with reduced latency by sending the most important data first.
Data Source
AI summary
Disclosed herein is a method for transmitting point cloud data, including encoding point cloud data, and transmitting a bitstream including the point cloud data. Disclosed herein is a method for receiving point cloud data, including receiving a bitstream including point cloud data, and decoding the point cloud data.