Point Cloud Attribute Entropy Coding with Adaptive Contexts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing point cloud compression methods, particularly in attribute compression, suffer from low compression performance due to direct encoding and decoding of attribute residual coefficients, leading to inefficient data handling and storage.
Innovation Solution
A novel point cloud attribute entropy encoding and decoding method that utilizes adaptive context-based encoding and decoding of residual coefficients, optimizing the utilization of redundant information to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If direct encoding and decoding methods are used for attribute residual coefficients, then the encoding process is simple, but the compression performance is low
Solution Approach 1:
The patent changes the encoding parameters from direct coefficient encoding to context-based probability modeling. By introducing context variables and probability models that adapt to local data characteristics, the encoding process transforms fixed-parameter direct encoding into variable-parameter context-based encoding, thereby improving compression performance while maintaining reasonable complexity
Solution Approach 2:
The patent introduces dynamic context adaptation where the encoding parameters are not fixed but adapt dynamically based on the local characteristics of the point cloud data. The context-based entropy encoder dynamically selects encoding strategies based on predicted values and residual characteristics, making the encoding process flexible and adaptive to different data patterns
2Productivity
If context-based adaptive encoding is used for attribute residual coefficients, then the compression performance is improved, but the encoding complexity increases
Solution Approach 1:
The patent segments the encoding process into distinct stages: prediction stage, residual calculation stage, and context-based entropy encoding stage. Each stage handles specific tasks independently, allowing for optimized implementation of each component and reducing overall complexity through modular design
Solution Approach 2:
The patent performs preliminary prediction operations before the actual encoding process. By generating predicted attribute values using LOD-based prediction methods beforehand, the system prepares the data in a form that is more suitable for subsequent context-based entropy encoding, reducing the complexity of the main encoding process
3Measurement precision
If the number of points in a point cloud frame is increased to improve precision, then the data volume increases significantly, but the compression efficiency decreases
Solution Approach 1:
The patent extracts and separates the attribute information from the geometric point cloud data, applying specialized entropy encoding methods tailored for attribute data. By extracting the attribute residuals and applying context-based encoding specifically to these residuals, the system efficiently compresses the attribute portion independent of the geometric data
Solution Approach 2:
The patent introduces prediction values as an intermediary between the original attribute values and the encoded residuals. These predicted values serve as a reference that captures the correlation between points, allowing the actual encoding to focus only on the differences (residuals), thereby significantly reducing the data volume while preserving precision
Data Source
AI summary
The present invention provides a point cloud attribute entropy encoding method and device, and a point cloud attribute entropy decoding method and device. The encoding method comprises: if an overall encoding processing condition of point cloud attribute residual coefficients is satisfied, performing overall encoding processing on the point cloud attribute residual coefficients, and then ending the encoding; and if not, performing local one-by-one encoding processing on the point cloud attribute residual coefficients until the encoding is ended. The entropy decoding method comprises: if an overall decoding processing condition of the point cloud attribute residual coefficients is satisfied, performing overall decoding processing on the point cloud attribute residual coefficients, and then ending the decoding; and if not, performing local one-by-one decoding processing on the point cloud attribute residual coefficients until the decoding is ended. The present invention better utilizes redundant information in a point cloud and improves compression performance.