Point Cloud Attribute Prediction Using Parent Neighbor Thresholds
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Solution Overview
Problem
In the Geometry-based Point Cloud Compression (G-PCC) encoding and decoding framework, the attribute coding of point clouds is inefficient due to inadequate conditions for starting prediction coding, leading to low attribute coding efficiency.
Innovation Solution
Determine the number of neighboring nodes of a parent node to decide if attribute prediction is allowed, and use this information to calculate an attribute prediction value for child nodes based on neighboring nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prediction coding is performed only when all start-up conditions are met, then the encoding process is simple, but the attribute coding efficiency is low
Solution Approach 1:
The patent changes the parameter of prediction coding start-up conditions by introducing a new condition based on the number of neighboring nodes of the parent node. This modifies the original start-up conditions to include a check on whether the parent node has sufficient neighboring nodes, thereby enabling prediction coding in additional cases without excessive complexity
Solution Approach 2:
The patent dynamically adjusts the prediction coding process by evaluating the number of neighboring nodes at each encoding step. Instead of using fixed start-up conditions, the system adaptively determines whether to perform prediction coding based on the local structural characteristics of the point cloud data, optimizing coding efficiency for different data configurations
2Productivity
If the number of neighboring nodes is checked before prediction coding, then attribute coding efficiency improves, but the encoding complexity increases
Solution Approach 1:
The patent introduces a new parameter (number of neighboring nodes) to the prediction coding decision process. This parameter is checked alongside existing start-up conditions to determine whether prediction coding should be performed, thereby improving coding efficiency while maintaining manageable complexity through a clear conditional structure
Data Source
AI summary
An encoding, a decoding method, and a storage medium are provided. The method includes: determining the number of parent node neighborhood nodes of a current node; when the number of parent node neighborhood nodes of the current node is greater than or equal to a preset threshold value, determining that the current node allows attribute prediction; and determining a predicted attribute value of a child node of the current node on the basis of attribute information of the neighborhood nodes of the current node.