Point Cloud Bitstream Compression With Hierarchical Depth Maps
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing large amounts of point cloud data required for applications such as virtual reality, augmented reality, and self-driving services, due to high latency and encoding/decoding complexity.
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
1Device complexity
If geometry-based point cloud compression coding is used, then encoding/decoding complexity is reduced, but compression efficiency may be compromised
Solution Approach 1:
The patent segments the point cloud data into multiple depth maps at different resolution levels. The encoder processes only the difference information between adjacent depth maps rather than encoding complete depth maps, significantly reducing encoding complexity while maintaining compression efficiency through hierarchical structure.
Solution Approach 2:
The patent applies partial action by encoding only the difference information between adjacent depth maps at different resolutions, rather than encoding complete depth maps. This partial encoding approach reduces computational complexity while maintaining sufficient compression efficiency for the application.
2Loss of energy
If video-based point cloud compression coding is used, then compression efficiency is improved, but encoding/decoding complexity increases
Solution Approach 1:
The patent segments the compression process into multiple stages by creating depth maps at different resolution levels. Each stage encodes only the difference information from the previous stage, dividing the complex video-based compression into manageable segments that reduce overall computational burden.
Solution Approach 2:
The patent performs preliminary action by pre-processing the point cloud data into multiple depth maps at different resolutions before the actual compression. This preliminary organization of data into hierarchical depth maps enables more efficient subsequent encoding by establishing a structured foundation for difference-based compression.
3Reliability
If feedback information is incorporated to optimize data processing, then service quality is improved, but system complexity increases
Solution Approach 1:
The patent incorporates feedback mechanisms where the encoder receives feedback information about the decoded output and adjusts the encoding process accordingly. This feedback loop enables optimization of compression parameters based on actual service quality requirements, improving reliability while managing system complexity through controlled feedback integration.
Data Source
AI summary
Disclosed herein is a method for receiving point cloud data, including receiving a bitstream containing the point cloud data, and decoding the point cloud data. Disclosed herein is a method for transmitting point cloud data, including encoding the point cloud data, and transmitting a bitstream containing the point cloud data.