Point Cloud Quantization Control for Bandwidth-Limited Reconstruction
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Solution Overview
Problem
In geometry-based point cloud compression (G-PCC), the quantization parameter is often set based on user experience, failing to achieve optimal reconstruction quality under specific transmission bandwidth conditions, and does not account for varying point densities in different regions.
Innovation Solution
Adaptive setting of the quantization parameter based on feature values of the current point cloud, enabling optimal reconstruction quality under limited bit rates by determining a first feature value and then setting the quantization parameter accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the quantization parameter is set based on user experience, then the encoding process is simple, but the reconstruction quality is not optimal under specific transmission bandwidth conditions
Solution Approach 1:
The patent changes the quantization parameter dynamically based on point cloud characteristics (such as point density, geometry complexity, and attribute variance) rather than using fixed experience-based values. This allows the encoding system to adapt parameters to match specific transmission bandwidth conditions and point cloud features, thereby optimizing reconstruction quality while maintaining encoding simplicity.
2Productivity
If a fixed quantization parameter is used, then the encoding process is efficient, but it fails to account for varying point densities in different regions
Solution Approach 1:
The patent applies different quantization parameters to different regions of the point cloud based on local characteristics such as point density, geometric complexity, and attribute variation. High-density regions with simple geometry use coarser quantization, while low-density regions with complex features use finer quantization. This local adaptation maintains encoding efficiency while significantly improving the handling of varying point densities across the point cloud.
3Quantity of substance
If the quantization parameter is set improperly, then the transmission bandwidth requirement is not met, but the reconstruction quality cannot be optimized
Solution Approach 1:
The patent implements dynamic quantization parameter adjustment based on the relationship between transmission bandwidth requirements and point cloud characteristics. The system continuously adapts quantization parameters during encoding to meet specific bandwidth constraints while maximizing reconstruction quality. This dynamic approach allows proper bandwidth utilization and optimal quality achievement by adjusting parameters in real-time according to actual transmission conditions.
Data Source
AI summary
Disclosed in the embodiments of the present application are an encoding method, a decoding method, a code stream, an encoder, a decoder, and a storage medium. The decoding method comprises: decoding a code stream and determining a value of preset identification information; when the preset identification information indicates that a first quantization parameter of the current point cloud enables a target setting mode, determining a first feature value according to the current point cloud; and determining a value of the first quantization parameter according to the first feature value.