Point Cloud Coordinate-Transformation Residual Encoding with Azimuth Contexts
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Solution Overview
Problem
Existing point cloud encoding/decoding technologies inefficiently encode coordinate transformation residuals due to the lack of consideration of the relationship between azimuth information and other residual components, leading to inaccurate context models and low encoding efficiency.
Innovation Solution
Establish a relationship between azimuth information and coordinate transformation residuals, divide the azimuth into intervals, and set corresponding encoding/decoding rules for each interval to improve entropy encoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a traversal method is used to encode coordinate transformation residuals component by component, then the encoding process is simple, but the encoding efficiency is low due to inaccurate context models
Solution Approach 1:
The patent segments the azimuth information into multiple intervals and creates separate context models for each interval. This allows the encoding process to handle different residual components with specialized context models tailored to their specific azimuth ranges, improving encoding efficiency while maintaining processability through systematic segmentation.
Solution Approach 2:
The patent applies local quality by creating different context models for different azimuth intervals. Each context model is optimized for the specific characteristics of residual components within its designated azimuth range, allowing for more accurate and efficient encoding of each local region rather than using a single uniform context model.
2Device complexity
If only coordinate transformation residual component information is used to establish entropy encoding context models, then the context model establishment is simple, but the context model accuracy is insufficient
Solution Approach 1:
The patent introduces azimuth information as an additional dimension to the context model establishment process. By incorporating azimuth intervals as a new dimension of classification, the system can distinguish between different residual components based on their angular position, significantly improving context model accuracy without excessive complexity increase.
Solution Approach 2:
The patent uses azimuth information as an intermediary factor to bridge the gap between residual component encoding and spatial orientation. This intermediary dimension allows the system to leverage azimuth data to create more accurate context models for residual encoding, improving measurement precision through the mediating role of azimuth intervals.
Data Source
AI summary
An encoding method includes: obtaining original point cloud data; transforming coordinates of the original point cloud data to obtain a coordinate transformation residual; establishing a relationship between azimuth information of a point cloud and the coordinate transformation residual; determining an encoding rule for the coordinate transformation residual based on the relationship between the azimuth information of the point cloud and the coordinate transformation residual; and encoding the coordinate transformation residual according to the encoding rule, to obtain data rate information.