Unified Predictive Tree for Point Cloud Geometry and Attribute Compression
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Solution Overview
Problem
Current methods for processing point cloud data face challenges in efficiently transmitting and receiving large amounts of data, particularly in terms of latency and encoding/decoding complexity, especially for geometry-point cloud compression (G-PCC) bitstreams.
Innovation Solution
A method involving the division of point cloud data into compression units, sorting based on similarity, and generating a predictive tree for prediction-based compression, allowing for simultaneous geometry and attribute coding using the same or different predictive coding parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If point cloud data is compressed using traditional methods, then compression is achieved, but latency and encoding/decoding complexity increase
Solution Approach 1:
The point cloud data is divided into multiple compression units (CU), and each CU is further divided into prediction units (PU) and transformation units (TU). This hierarchical segmentation enables parallel processing of different units, reducing overall encoding/decoding latency while maintaining compression efficiency through localized processing.
Solution Approach 2:
A predictive tree structure is constructed beforehand based on geometry information before attribute compression. This preliminary action organizes the spatial relationships and enables faster prediction during the actual compression process, reducing both latency and computational complexity.
2Measurement precision
If separate predictive trees are used for geometry and attribute coding, then accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges the geometry predictive tree and attribute predictive tree into a single unified predictive tree structure. This unified tree shares the same hierarchical organization and spatial relationships for both geometry and attribute coding, reducing device complexity while maintaining coding accuracy through shared prediction pathways.
Solution Approach 2:
The unified predictive tree serves multiple functions simultaneously: it provides the hierarchical structure for geometry coding and the same structure for attribute coding. This multi-functionality eliminates the need for separate tree constructions and reduces overall system complexity while preserving the precision needed for both coding types.
3Productivity
If point cloud data is divided and processed in multiple units, then compression efficiency is improved, but processing complexity increases
Solution Approach 1:
Point cloud data is segmented into compression units (CU) that are further divided into prediction units (PU) and transformation units (TU). This multi-level segmentation enables parallel processing across different units, improving compression speed through pipelining while managing complexity through standardized processing templates for each unit type.
Solution Approach 2:
The patent employs configurable parameters such as CU size, PU size, and TU size that can be adjusted based on the specific point cloud characteristics. This parameter flexibility allows optimization of the balance between compression speed and processing complexity for different application scenarios without fundamentally changing the processing architecture.
Data Source
AI summary
A point cloud data transmission method according to embodiments comprises the steps of: encoding point cloud data; and transmitting signaling data and the encoded point cloud data, wherein the step for encoding may comprise the steps of: dividing the point cloud data into a plurality of compression units; sorting, for each compression unit, the point cloud data in each compression unit; generating a prediction tree on the basis of the sorted point cloud data in the compression units; and compressing the point cloud data in the compression units by predicting on the basis of the prediction tree.