Point Cloud Context Model Spatial Correlation Coding
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Solution Overview
Problem
Current point cloud coding frameworks do not fully utilize spatial correlation when constructing context models, leading to reduced efficiency in encoding and decoding processes.
Innovation Solution
The method involves obtaining occupancy bit information from neighboring nodes to determine a context model, which is used for entropy encoding and decoding of flag and coordinate information of single child nodes, fully exploiting spatial correlation to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spatial correlation is not fully utilized when constructing context models, then the encoding and decoding process is simpler, but the coding efficiency is reduced
Solution Approach 1:
The patent performs preliminary classification of neighboring node occupancy patterns into five categories (first to fifth types) based on the number and positions of occupied neighboring nodes. This preliminary action enables the selection of appropriate context models before actual encoding, improving coding efficiency by utilizing spatial correlation while maintaining manageable complexity through pre-defined classification rules.
Solution Approach 2:
The patent applies different context models (first context model, second context model, third context model) to different types of neighboring node configurations. By matching specific context models to specific occupancy patterns, the system optimizes the local encoding quality for each spatial configuration, thereby improving overall coding efficiency without requiring a single complex universal model.
2Measurement precision
If occupancy bit information of neighbouring nodes is used to determine context model, then spatial correlation is fully utilized, but the encoding process becomes more complex
Solution Approach 1:
The patent segments the neighboring node occupancy patterns into five distinct types based on the number of occupied neighboring nodes (first type: 0 occupied, second type: 1 occupied, third type: 2 occupied, fourth type: 3 occupied, fifth type: 4 or more occupied). This segmentation simplifies the complex spatial correlation analysis by dividing it into manageable categories, each with its own context model, thus improving measurement precision while controlling encoding complexity.
Solution Approach 2:
The patent changes the parameter of context model selection based on the occupancy bit information of neighboring nodes. By dynamically selecting different context models according to the occupancy pattern parameters, the system achieves precise utilization of spatial correlation while maintaining encoding process manageability through parameter-driven model selection rather than complex algorithmic processing.
Data Source
AI summary
A point cloud coding method is provided. The method includes the following. Occupancy bit information of neighbouring nodes of a current node is obtained. A context model is determined according to the occupancy bit information of the neighbouring nodes. Related information of the current node is entropy encoded with the context model and signalled into a bitstream, where the related information includes at least one of flag information of a single child node or coordinate information of the single child node.