G-PCC Planar Node Coding Using Neighborhood Structure
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Solution Overview
Problem
In existing Geometry-Based Point Cloud Compression (G-PCC) frameworks, the geometric coding efficiency is reduced due to incomplete consideration of planar position information during predictive coding, particularly when encoding and decoding planar nodes based on partial prior reference information.
Innovation Solution
Determine planar structure information of neighborhood nodes, use context indication information to determine target context information, and encode/decode planar position information of the current node based on this context, considering the correlation between neighborhood nodes to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predictive coding is performed on planar position information of the current node only based on partial prior reference information, then the encoding process is simplified, but the geometric coding efficiency is reduced
Solution Approach 1:
The patent performs preliminary determination of planar structure information for neighborhood nodes before encoding the current node. By pre-processing and storing the planar structure information of neighboring nodes (such as whether they are planar nodes and their planar position information), the encoder can efficiently select appropriate context models during the encoding process, thus improving geometric coding efficiency without significantly increasing overall encoding complexity.
Solution Approach 2:
The patent enables the encoding system to automatically adapt to local geometric characteristics by using the determined planar structure information of neighborhood nodes to select context models. The system serves itself by utilizing the spatial correlation and geometric continuity inherent in point cloud data, automatically adjusting encoding parameters based on the local planar structure without requiring external intervention or complex manual configuration.
2Productivity
If planar structure information of neighborhood nodes is fully considered, then the geometric coding efficiency is improved, but the context modeling complexity increases
Solution Approach 1:
The patent applies different context models based on the local planar structure characteristics of neighborhood nodes. Instead of using a uniform context model for all nodes, the system determines the planar structure information of neighboring nodes and selects appropriate context models (such as context_model_0, context_model_1, context_model_2) based on the specific local geometry. This localized approach improves coding efficiency by adapting to local characteristics while keeping the overall system manageable through a finite set of context models.
Solution Approach 2:
The patent changes the context model parameters dynamically based on the planar structure information of neighborhood nodes. By determining whether neighboring nodes are planar and their planar positions, the system adjusts the context model selection and parameters accordingly. This parameter adaptation allows the encoder to optimize for local geometric features, improving geometric coding efficiency while maintaining controlled complexity through systematic parameter management.
Data Source
AI summary
Embodiments of the present disclosure disclose a decoding method. The method includes: determining planar structure information of neighborhood nodes of a current node; determining context indication information of the current node according to the planar structure information of the neighborhood nodes; determining target context information according to the context indication information; and decoding a bitstream based on the target context information to determine planar position information of the current node.