Point Cloud Decoding with Selective Node Splitting and Motion Compensation
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Solution Overview
Problem
The surge in internet videos and higher video clarity requirements have led to a need for improved digital video compression technology to reduce bandwidth and traffic pressure, particularly in managing large volumes of point cloud data which are constrained by storage space and transmission bandwidth.
Innovation Solution
A decoding method and encoding method are provided that determine whether to split a current node, perform motion compensation, and encode a flag indicating the split, while utilizing geometric position information to optimize point cloud compression, along with a non-transitory computer-readable storage medium to implement these methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If motion compensation is performed on point cloud data, then transmission bandwidth is reduced, but computational complexity increases
Solution Approach 1:
The point cloud data is divided into multiple nodes that can be independently processed. Each node is evaluated separately to determine whether motion compensation should be applied, allowing selective processing that reduces overall computational complexity while maintaining bandwidth efficiency.
Solution Approach 2:
Different processing strategies are applied to different regions of the point cloud data based on local characteristics. The split_flag parameter enables different nodes to have different processing qualities, with motion compensation applied only where beneficial, rather than uniformly across all data.
2Productivity
If node splitting is implemented in point cloud compression, then compression efficiency is improved, but processing time increases
Solution Approach 1:
The point cloud is segmented into discrete nodes with split_flag parameters that control further subdivision. This hierarchical segmentation allows compression efficiency to be improved through selective splitting only where necessary, rather than uniformly processing all data to the finest detail level.
Solution Approach 2:
Instead of applying full node splitting uniformly across all point cloud data, the method applies splitting selectively based on the split_flag parameter. This partial action approach achieves sufficient compression efficiency without the excessive processing time that would result from complete hierarchical decomposition.
3Quantity of substance
If geometric position information is optimized through selective node processing, then storage space is reduced, but decoding complexity increases
Solution Approach 1:
The method extracts and processes only the necessary geometric position information for each node based on the split_flag parameter. By taking out only the essential data elements rather than processing complete node hierarchies, storage space is reduced without proportionally increasing decoding complexity.
Solution Approach 2:
Instead of processing all nodes uniformly and then filtering, the method inverts the approach by using the split_flag parameter to pre-identify which nodes require processing. This inversion allows the decoder to skip unnecessary processing steps, reducing both storage requirements and decoding complexity.
Data Source
AI summary
A decoding method, an encoding method, and a non-transitory computer-readable storage medium are provided. The decoding method includes the following. Whether to split a current node in a current point cloud is determined. A motion parameter of the current node is determined by decoding a bitstream in the case where the current node is not to be split. A compensation node for the current node is determined by performing motion compensation on a reference node for the current node based on the motion parameter of the current node. A prediction node for the current node is determined based on the compensation node for the current node. Geometric position information of the current point cloud is determined based on the prediction node for the current node.