Point Cloud Attribute Encoding Using Prediction-Mode Context
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Solution Overview
Problem
Existing point cloud encoding methods suffer from low encoding efficiency due to direct entropy encoding on attribute prediction residuals of each point, which does not utilize the correlation between attribute prediction modes and residuals.
Innovation Solution
Implementing entropy encoding on attribute prediction residuals using attribute prediction mode information as a context, thereby utilizing the correlation between the prediction mode and residual to improve encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If entropy encoding is performed directly on attribute prediction residuals of each point, then the encoding process is simple, but the encoding efficiency is low
Solution Approach 1:
The patent changes the parameter used for entropy encoding from the raw attribute prediction residual to the residual after applying a transform (such as DCT or wavelet transform). This transformation reorganizes the residual data to highlight correlations and patterns, enabling more efficient entropy encoding while maintaining reasonable process complexity through standard transform algorithms.
Solution Approach 2:
The patent applies a transform operation as a preliminary action before entropy encoding. This preliminary transformation of the attribute prediction residual into a transformed residual prepares the data in a more suitable form for subsequent entropy encoding, improving encoding efficiency by pre-organizing the data to better exploit statistical correlations.
2Productivity
If attribute prediction mode information is used as context for entropy encoding, then encoding efficiency improves, but the encoding process becomes more complex
Solution Approach 1:
The patent employs feedback by using the attribute prediction mode information as context for the entropy encoding process. The prediction mode information provides contextual clues about the statistical characteristics of the residual data, allowing the entropy encoder to adapt its parameters accordingly. This feedback mechanism improves encoding efficiency by leveraging the correlation between prediction modes and residuals while maintaining manageable complexity through standardized context modeling techniques.
Data Source
AI summary
This application discloses a point cloud encoding processing method, a point cloud decoding processing method, and a related device. The point cloud encoding processing method in embodiments of this application includes: determining an attribute prediction mode of attribute information of a to-be-encoded point; obtaining an attribute prediction residual of the attribute information of the to-be-encoded point based on the attribute prediction mode; and performing entropy encoding on the attribute prediction residual by using attribute prediction mode information as a context, to obtain an encoding result of the to-be-encoded point, where the attribute prediction mode information indicates the attribute prediction mode.