3D Point Cloud Attribute Coding With Threshold-Based Prediction
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Solution Overview
Problem
Existing three-dimensional data encoding and decoding methods face inefficiencies in coding processes, particularly in managing large volumes of point cloud data, necessitating improved compression techniques.
Innovation Solution
A three-dimensional data encoding and decoding method that determines the use of prediction processes based on a threshold value for valid nodes in an N-ary tree structure, allowing for efficient encoding and decoding by selectively using parent nodes for attribute information processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prediction process is applied to all nodes in point cloud data, then coding efficiency is improved, but processing complexity and time consumption increase
Solution Approach 1:
The patent applies different encoding strategies to different nodes based on their specific characteristics. Nodes with sufficient valid node counts in higher layers receive prediction processing to improve coding efficiency, while nodes with insufficient valid node counts skip prediction to reduce processing time. This local differentiation resolves the contradiction by applying prediction only where beneficial.
Solution Approach 2:
The patent introduces a threshold parameter (first threshold value for valid node count) that dynamically determines whether prediction processing should be applied. By changing the parameter of applying prediction based on the valid node count condition, the system optimizes the balance between coding efficiency and processing time, resolving the technical contradiction.
2Productivity
If prediction process uses parent nodes from same layer, then coding efficiency improves, but device complexity increases
Solution Approach 1:
The patent makes the encoding structure adaptable by allowing nodes to serve different functions based on conditions. The same node can be used for prediction references when valid node counts are sufficient, or skipped when they are not. This multi-functionality approach maintains coding efficiency benefits while reducing complexity when prediction is not applicable.
Solution Approach 2:
The patent introduces dynamic adaptability in the encoding structure through conditional prediction application. The encoding process dynamically adjusts whether to use parent nodes from the same layer based on the valid node count threshold, making the system flexible and adaptable rather than rigid, thus managing complexity while maintaining efficiency.
3Productivity
If prediction process is applied to nodes with insufficient valid nodes, then coding efficiency improves, but data accuracy and reliability deteriorate
Solution Approach 1:
The patent evaluates the local quality (valid node count) of each node and applies prediction processing selectively. Nodes with sufficient valid node counts receive prediction for improved coding efficiency, while nodes with insufficient valid node counts skip prediction to maintain data accuracy. This local quality assessment resolves the contradiction between efficiency and reliability.
Solution Approach 2:
The patent uses feedback from valid node count assessment to determine whether prediction processing should be applied. The system continuously monitors the valid node counts in higher layers and uses this feedback to dynamically decide on prediction application, ensuring that prediction is only applied when it improves both efficiency and maintains data accuracy.
Data Source
AI summary
A three-dimensional data encoding method includes: determining whether a first valid node count is greater than or equal to a first threshold value predetermined, the first valid node count being a total number of valid nodes that are nodes each including a three-dimensional point, the valid nodes being included in first nodes belonging to a layer higher than a layer of a current node in an N-ary tree structure of three-dimensional points included in point cloud data, N being an integer greater than or equal to 2; and, when the first valid node count is greater than or equal to the first threshold value, performing first encoding on attribute information of the current node, the first encoding including a prediction process in which second nodes are used, the second nodes including a parent node of the current node and belonging to a same layer as the parent node.