Context-Based Entropy Encoding for Point Cloud Residual Radius
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Solution Overview
Problem
Existing point cloud compression technologies face challenges in efficiently encoding and decoding point clouds, particularly for sparse geometry data captured by spinning sensor heads, which requires simple and low-latency encoding while maintaining high compression performance.
Innovation Solution
The method involves obtaining a predicted radius for each point in the point cloud from prediction data, calculating a residual radius, selecting a context based on the prediction data, and using context-based entropy encoding and decoding to efficiently represent the residual radius in a bitstream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If context-based entropy encoding is used for residual radius, then compression performance is improved, but encoding complexity increases
Solution Approach 1:
The patent applies parameter changes by adapting the entropy encoding context based on prediction data characteristics. Different contexts are selected or created according to the predicted radius values and their statistical properties, allowing the encoder to optimize compression efficiency for different spatial regions and objects without using a single fixed encoding scheme.
Solution Approach 2:
The patent uses context copying where probability models or context tables are copied and reused for encoding residual radii with similar statistical characteristics. This allows efficient encoding without requiring complex real-time model building, as pre-computed or previously used contexts can be directly applied to similar data patterns.
2Productivity
If prediction data is used for context selection, then encoding efficiency is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-computing context tables or probability models based on prediction data before the actual encoding process. This allows the encoder to quickly select from pre-prepared contexts during real-time encoding, avoiding the need for complex calculations at encoding time while maintaining high compression efficiency.
Solution Approach 2:
The patent replaces complex real-time calculations with simpler table lookups and context selections. Instead of performing complex probability calculations during encoding, the system uses pre-computed context tables that can be quickly accessed and applied, substituting mechanical computation with lookup operations that are much faster in practice.
Data Source
AI summary
A method of encoding a point cloud into a bitstream of encoded point cloud data, each point of the point cloud being associated with a radius responsive to a distance of the point from a sensor that captured the point. The method includes: obtaining a predicted radius representative of a prediction of a radius of a point of the point cloud from prediction data (PD); obtaining a residual radius of a point of the point cloud between the radius of said point and the predicted radius; selecting a context based on the prediction data (PD); and context-based entropy encoding the magnitude of the residual radius based the selected context.