Point Cloud Attribute Coding with Dual Context Selection
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Solution Overview
Problem
Existing point cloud encoding/decoding schemes have inefficient attribute encoding/decoding due to non-optimized context model selection, leading to low encoding/decoding efficiency.
Innovation Solution
A method that involves obtaining an attribute residual, binarizing it to get binary codes for flag bits, and selecting specific context models based on different conditions to encode these binary codes, thereby optimizing the context model selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single context model is used for encoding both flag bits, then the device complexity is reduced, but the attribute encoding efficiency deteriorates
Solution Approach 1:
The patent divides the encoding process into two separate context model selections: one for the first flag bit and another for the second flag bit. This segmentation allows each flag bit to be encoded with an optimized context model tailored to its specific characteristics, thereby improving attribute encoding efficiency while maintaining manageable device complexity through systematic organization of the encoding steps.
2Productivity
If different context models are selected for different flag bits, then the attribute encoding efficiency is improved, but the device complexity increases
Solution Approach 1:
The patent applies local quality by selecting different context models suited to the specific characteristics of each flag bit. The first context model is optimized for the first flag bit while the second context model is optimized for the second flag bit, allowing each part of the encoding process to have the quality and characteristics best suited to its specific requirements, thereby improving overall encoding efficiency.
Solution Approach 2:
The patent introduces dynamic context model selection where the context model is not fixed but is selected based on specific conditions and parameters of the attribute data being encoded. This dynamic approach allows the encoding system to adapt to different data characteristics, improving encoding efficiency while the systematic selection process keeps the increased complexity manageable.
3Manufacturing precision
If arithmetic encoding with context model is used, then the encoding precision is improved, but the coding complexity increases
Solution Approach 1:
The patent utilizes parameter changes by varying the context model parameters based on the specific characteristics of the attribute data and flag bits being encoded. By adjusting and selecting appropriate context model parameters for different encoding scenarios, the system achieves high encoding precision while the systematic parameter selection process keeps the coding complexity manageable through organized parameter management.
Data Source
AI summary
A data encoding method includes obtaining an attribute residual of a current point cloud point, binarizing the attribute residual to obtain a binary code of the current point cloud point that includes a first binary code indicating a first flag bit and a second binary code indicating a second flag bit, selecting a first context model from a context model list according to a first condition, selecting a second context model from the context model list according to a second condition, encoding the first binary code using the first context model, and encoding the second binary code using the second context model. The first condition and the second condition are different for the first context model and the second context model corresponding to a same index in the context model list.