Point Cloud Attribute Coding Using Prediction-Mode Contexts
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Solution Overview
Problem
Existing point cloud encoding technologies suffer from low encoding efficiency due to direct entropy encoding on attribute prediction residuals without considering the correlation between the residuals and their prediction modes.
Innovation Solution
Determine the attribute prediction mode of a point cloud and use this mode's information as a context for entropy encoding and decoding, thereby improving the encoding efficiency by leveraging the correlation between the prediction mode and residual.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct entropy encoding is performed on attribute prediction residual of each point, then the encoding process is simple, but the encoding efficiency is low
Solution Approach 1:
The patent performs preliminary classification of attribute prediction residuals into different types based on their characteristics before entropy encoding. This preliminary action groups similar residuals together, enabling more efficient subsequent encoding processing and improving overall encoding efficiency without excessive complexity increase
Solution Approach 2:
The patent changes the encoding parameter by using different entropy encoding contexts or methods for different types of attribute prediction residuals. Instead of uniform encoding, it adapts the encoding parameters based on residual characteristics, thereby improving encoding efficiency while maintaining manageable process complexity
2Loss of information
If uniform entropy encoding is applied to all attribute prediction residuals, then the encoding method is straightforward, but it fails to exploit correlations between residuals and prediction modes
Solution Approach 1:
The patent applies different entropy encoding strategies to different types of attribute prediction residuals based on their local characteristics and correlations with prediction modes. This localized approach exploits specific correlations in each residual type while maintaining overall system manageability, improving information compression efficiency without excessive complexity
Solution Approach 2:
The patent performs preliminary classification of residuals into different types before encoding, which enables the subsequent encoding process to exploit correlations specific to each type. This preliminary categorization action allows the system to capture information patterns more effectively while keeping the encoding method complexity controlled
Data Source
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AI summary
This application discloses a point cloud encoding processing method, a point cloud decoding processing method, and a related device, and belongs to the field of computer technologies. 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.