Point Cloud Predictive Tree Encoding With Adaptive Bit Parameters
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Solution Overview
Problem
Current point cloud encoding methods using predictive tree encoding with fixed encoding parameters result in encoding redundancy, leading to low efficiency.
Innovation Solution
Adaptively determine a first parameter based on the size of the to-be-encoded point cloud block to construct a predictive tree and perform entropy encoding on the prediction residual, reducing encoding redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a preset fixed encoding parameter is used to encode prediction residual, then the encoding process is simple, but encoding redundancy increases and encoding efficiency decreases
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed encoding parameter to an adaptive parameter that varies based on the size of the point cloud block. The encoding parameter is dynamically determined according to the actual data characteristics, allowing the system to optimize encoding efficiency for different block sizes while maintaining implementation feasibility through standardized adaptation rules.
Solution Approach 2:
The patent implements parameter changes by modifying the encoding parameter based on the point cloud block size. Different block sizes correspond to different encoding parameters, which are selected adaptively during the encoding process. This parameter adaptation reduces encoding redundancy and improves encoding efficiency without significantly complicating the overall encoding process.
2Device complexity
If a preset fixed encoding parameter is used, then the device complexity is low, but encoding redundancy increases
Solution Approach 1:
The patent applies parameter changes by adapting the encoding parameter to the point cloud block size, which reduces encoding redundancy. The adaptation follows predefined rules that map block sizes to appropriate encoding parameters, maintaining relatively low device complexity while significantly reducing the loss of information during encoding.
Solution Approach 2:
The patent implements feedback by using the actual point cloud block size information to adjust the encoding parameter. This feedback mechanism allows the encoding system to optimize its performance based on real data characteristics, reducing redundancy without requiring overly complex decision-making logic or additional hardware.
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 according to an embodiment of this application includes: determining a to-be-encoded point cloud block based on geometric information of a point cloud, and determining a first parameter based on the to-be-encoded point cloud block, where the first parameter is used to indicate the number of bits required to encode a second parameter, and the second parameter is used to indicate a size of the to-be-encoded point cloud block; constructing a predictive tree corresponding to the to-be-encoded point cloud block, and determining a prediction residual of a node in the predictive tree; and performing entropy encoding on the prediction residual based on the first parameter to obtain a geometry encoding result.