Point Cloud Azimuthal Coding Context Selection
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Solution Overview
Problem
The existing point cloud compression methods, such as V-PCC and G-PCC, face inefficiencies in compressing sparse geometry data, particularly due to high computational costs and complex encoding processes, which hinder effective entropy coding and decoding in azimuthal coding modes.
Innovation Solution
The proposed method improves context-based entropy coding by selecting contexts based on an apparent angle representing the interval angle seen from a sensor, allowing for more accurate probability estimation and efficient encoding/decoding of point cloud coordinates using an azimuthal coding mode, which reduces computational complexity and enhances compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing point cloud compression methods (V-PCC, G-PCC) are used, then compression is achieved, but computational cost is high and encoding process is complex
Solution Approach 1:
The patent changes the parameter used for context selection from absolute angle values to apparent angle ranges. By categorizing apparent angles into discrete ranges (e.g., 0-45 degrees, 45-90 degrees), the system reduces the complexity of context-based entropy coding while maintaining compression efficiency, directly addressing the contradiction between computational cost and compression performance
Solution Approach 2:
The patent applies different context models based on local geometric characteristics of the point cloud data. By identifying regions with different apparent angle distributions and applying specialized context models to each region, the system achieves better compression without uniformly increasing computational complexity across all data
2Manufacturing precision
If context-based entropy coding is used with azimuthal coding mode, then coding performance is improved, but context selection accuracy is insufficient
Solution Approach 1:
The patent performs preliminary calculation of apparent angles and their ranges before the actual entropy coding process. By pre-computing and storing apparent angle information, the system ensures accurate context selection is available when needed, improving both context selection accuracy and overall coding performance without adding computational burden during the main encoding phase
Solution Approach 2:
The patent introduces apparent angle ranges as an intermediary between the raw geometric data and the context selection process. This intermediary layer abstracts the complex angular relationships into manageable categories, enabling more accurate context selection while simplifying the overall coding process
Data Source
AI summary
A method of encoding a point cloud into a bitstream of encoded point cloud data representing a physical object is provided. The method includes an azimuthal coding mode providing a series of bits for encoding a coordinate of a point of the point cloud. The method includes: dividing an interval, to which the point coordinate belongs to, into a left half interval and a right half interval; selecting a context based on an apparent angle (AAd) representing an interval angle seen from a sensor that captured the point; and context-adaptive binary entropy encoding a bit (bd) of the series of bits, into the bitstream, based on the selected context, the encoded bit (bd) indicating which of the two half intervals the point coordinate belongs to.