Spinning Sensor Point Cloud Coding With Residual Sign Prediction
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Solution Overview
Problem
Existing point cloud codecs, such as G-PCC, struggle to efficiently compress sparse geometry data captured by a spinning sensor head due to the continuous nature of the radius and inefficient entropy coding of residual radius, leading to a significant portion of the bitstream being dedicated to radius data, which hinders overall compression efficiency.
Innovation Solution
Entropy encoding/decoding the sign of the residual radius based on the sign of a previously encoded/decoded non-zero residual radius, leveraging the natural monotonicity of radius values captured by a spinning sensor head to reduce the coding cost of the signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If existing point cloud codecs (G-PCC) are used to compress sparse geometry data captured by a spinning sensor head, then the continuous nature of radius and inefficient entropy coding of residual radius are handled, but a significant portion of the bitstream is dedicated to radius data, which hinders overall compression efficiency
Solution Approach 1:
The patent changes the parameter representation by separating the radius into a sign component and a magnitude component. The sign is entropy-coded based on the natural monotonicity of radius values captured by spinning sensor heads, while the magnitude is encoded separately. This parameter transformation reduces the number of bits required to represent radius data, directly addressing the contradiction between bitstream size and compression efficiency.
2Productivity
If the sign of residual radius is encoded without leveraging natural monotonicity, then encoding simplicity is maintained, but the coding cost of signs increases, reducing overall coding efficiency
Solution Approach 1:
The patent implements feedback-based entropy coding for the sign of residual radius by utilizing the natural monotonicity property of radius values captured by spinning sensor heads. The encoder tracks the sign of previously encoded residual radius values and uses this information to predict the current sign, providing feedback that improves coding efficiency. This approach increases coding efficiency while maintaining manageable encoding complexity through the use of simple monotonicity-based prediction.
Data Source
AI summary
There is provided methods and apparatus of encoding/decoding a point cloud representing a physical object, each point of the point cloud being associated with a residual radius between a radius responsive to a distance of the point from a referential and a predicted radius. The method entropy encodes/decodes the sign of the residual radius associated with a current point of the point cloud based on the sign of a previously entropy encoded/decoded non-zero residual radius associated with a previous point.