3D Point Cloud Quantization for Efficient Decoding Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for encoding and decoding three-dimensional data lack efficiency, particularly in handling large amounts of point cloud data, which necessitates improved coding techniques for effective transmission and decoding.
Innovation Solution
A method involving quantizing geometry and attribute information of three-dimensional points using multiple quantization parameters, generating a bitstream that includes these quantized values and their differences, and employing inverse-quantization for decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed using existing encoding methods, then data transmission efficiency is improved, but decoding accuracy and quality deteriorate
Solution Approach 1:
The patent segments the point cloud data into multiple regions or groups, applying different quantization parameters to different segments. This allows important regions to maintain higher precision while less critical regions use aggressive compression, resolving the contradiction between overall compression efficiency and local decoding accuracy.
Solution Approach 2:
The patent implements local quality control by assigning different quantization parameters to different spatial regions or attribute types within the point cloud data. This ensures that critical areas maintain high fidelity while non-critical areas are compressed more aggressively, balancing transmission efficiency with decoding accuracy in a localized manner.
2Productivity
If multiple quantization parameters are used for geometry and attribute information, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent designs a unified encoding framework that handles both geometry and attribute information using a consistent multi-parameter quantization approach. This universal structure allows the same device architecture to efficiently process different data types with appropriate parameters, improving coding efficiency without proportionally increasing device complexity.
Solution Approach 2:
The patent dynamically adjusts quantization parameters based on the specific characteristics of the data being encoded (geometry vs. attributes, different spatial regions). By changing parameters adaptively rather than using fixed values, the system achieves higher coding efficiency while the parameter management overhead remains manageable through systematic control.
Data Source
AI summary
A three-dimensional data encoding method includes: quantizing geometry information of each of three-dimensional points, using a first quantization parameter; quantizing a first luminance using a second quantization parameter and quantizing a first chrominance using a third quantization parameter, the first luminance and the first chrominance indicating a first color among attribute information of each of the three dimensional points; and generating a bitstream including the geometry information quantized, the first luminance quantized, the first chrominance quantized, the first quantization parameter, the second quantization parameter, and a first difference between the second quantization parameter and the third quantization parameter.