Point Cloud Compression Using Dynamic Models for Inter-Frame Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current point cloud compression technologies, particularly geometric-structure-based point cloud compression (PCC), face inefficiencies in compressing dynamic images due to the lack of frame association, resulting in high data volumes and bandwidth limitations.
Innovation Solution
The method involves distinguishing point cloud data into a global point cloud set and object point cloud sets based on a reference frame, calculating dynamic models for each, and generating a bitstream that includes these models along with serial numbers, to enable efficient compression and decoding of point cloud data using inter-frame prediction and motion compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geometric-structure-based point cloud compression (PCC) is used, then compression is achieved, but compression efficiency is adversely affected due to lack of frame association
Solution Approach 1:
The point cloud data is segmented into multiple frames, with each frame containing point cloud data and associated dynamic models. This segmentation enables independent processing and compression of each frame while maintaining the ability to perform inter-frame prediction, thereby improving compression efficiency without excessive complexity
Solution Approach 2:
Dynamic models (including motion vectors and rotation information) are calculated and stored in advance for each frame. This preliminary action enables efficient inter-frame prediction during decoding, as the motion compensation can be performed using pre-computed dynamic models from reference frames, improving compression efficiency while managing complexity
2Quantity of substance
If point cloud data is compressed without frame association, then processing is simpler, but data volume remains high and bandwidth requirements increase
Solution Approach 1:
Dynamic models serve as intermediaries between reference frames and current frames. These models (containing motion vectors, rotation information, and point cloud data) enable efficient data transmission by allowing the decoder to reconstruct current frame data using reference frame data and the transmitted dynamic models, significantly reducing data volume while managing complexity
Solution Approach 2:
The patent transforms point cloud data into a different representation by calculating dynamic models that include motion vectors and rotation information. This parameter change allows the data to be compressed more efficiently, as the dynamic models capture the essential changes between frames, reducing the amount of data that needs to be transmitted
3Productivity
If motion vectors and dynamic models are integrated, then compression efficiency is enhanced, but encoding and decoding complexity increases
Solution Approach 1:
Motion vectors, rotation information, and point cloud data are merged into unified dynamic models. This merging consolidates multiple pieces of information into a single structured format, which improves compression efficiency by reducing redundant data while managing complexity through unified processing
Solution Approach 2:
The patent introduces dynamic models that adapt to the specific motion and transformation characteristics of each frame. This dynamic approach allows the compression algorithm to adjust to varying content requirements, improving compression efficiency while managing complexity through selective application of dynamic model calculation
Data Source
AI summary
An encoding method, a decoding method, and a device for point cloud compression are provided. The encoding method includes the following. Point cloud data corresponding to a first frame is obtained, and is distinguished into a global point cloud set and at least one object point cloud set according to a reference frame. The object point cloud set corresponds to at least one reference object point cloud set. A global dynamic model corresponding to the global point cloud set is calculated and an object dynamic model corresponding to the object point cloud set is calculated. A bitstream is generated. The bitstream includes the global point cloud set, the global dynamic model corresponding to the global point cloud set, a serial number of each object point in the reference object point cloud set, and the object dynamic model corresponding to the object point cloud set.