Point Cloud Occupancy Prediction Using Synchronous Tree Division
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The inter encoding scheme for point clouds in audio/video processing has low accuracy for inter prediction due to inefficient handling of occupancy statuses in context models.
Innovation Solution
Implement synchronous multi-branch tree division and inter prediction on occupancy statuses based on target values such as point count or density, using thresholds to predict child node occupancy and entropy encoding for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional inter encoding scheme is used with context models based on neighbor occupancy, then the encoding process can be simplified, but the inter prediction accuracy deteriorates
Solution Approach 1:
The patent changes the prediction parameters from binary occupancy status (0 or 1) to continuous density values calculated from point cloud data. By using density as the prediction parameter instead of simple occupancy, the system achieves higher prediction accuracy while maintaining manageable encoding complexity through standardized density calculation and threshold-based context modeling.
2Measurement precision
If more detailed context models with multiple neighbor checks are used, then inter prediction accuracy is improved, but the encoding complexity increases
Solution Approach 1:
The patent extracts only the essential density information from the point cloud data for prediction purposes, rather than examining all neighbor occupancy details. By taking out just the density parameter and using it to infer occupancy status through threshold comparison, the system reduces encoding complexity while preserving prediction accuracy.
3Productivity
If binary occupancy prediction is used, then the encoding process is faster, but the prediction accuracy and bit rate performance deteriorate
Solution Approach 1:
The patent performs preliminary density calculation on the reference point cloud frame before the actual occupancy prediction. By pre-computing density values and using them to determine prediction contexts, the system maintains fast encoding speed while improving accuracy, as the density-based prediction provides better initial estimates that reduce the need for complex subsequent processing.
Data Source
AI summary
A point cloud encoding/decoding processing method and apparatus, an encoding device, and a decoding device. The point cloud encoding processing method according to the embodiments of this application includes: performing, by an encoding end, synchronous multi-branch tree division on a current frame to be encoded and an encoded frame; and performing, by the encoding end based on a target value corresponding to a first node in the encoded frame, inter prediction on an occupancy status of a second node in the frame to be encoded, where the occupancy status includes: occupied or unoccupied, where a position of the first node in the encoded frame is the same as a position of the second node in the frame to be encoded.