Laser Angle Prediction for Lower G-PCC Coding Overhead
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Solution Overview
Problem
Existing point cloud compression techniques face inefficiencies in coding laser angles and azimuthal sampling locations, leading to increased coding overhead in the Geometry-based Point Cloud Compression (G-PCC) standard.
Innovation Solution
The proposed techniques involve determining predicted values based on first and second laser angles to efficiently decode or encode third laser angles, and using these predictions to reduce the data necessary for specifying laser angle differences and azimuthal sampling locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional point cloud compression techniques are used to code laser angles and azimuthal sampling locations, then the encoding process can be completed, but the coding overhead increases and compression efficiency decreases
Solution Approach 1:
The patent applies prediction techniques where the encoder predicts laser angle values based on previously encoded angles and encodes only the difference (residual) rather than the full value. This preliminary prediction action reduces the amount of data that needs to be encoded, thereby reducing coding overhead and improving compression efficiency.
Solution Approach 2:
The patent changes the parameter being encoded from the absolute laser angle value to the difference between the current angle and the predicted angle. This parameter transformation reduces the entropy of the data and allows for more efficient encoding, directly addressing the coding overhead problem.
2Measurement precision
If more data is used to specify laser angles and azimuthal sampling locations, then the precision and completeness of the point cloud representation is improved, but the data size increases and compression performance deteriorates
Solution Approach 1:
The prediction mechanism uses previously encoded angle information to estimate the current angle, allowing the system to maintain precision by encoding only the small difference rather than the full angle value. This preserves measurement precision while significantly reducing the quantity of data that needs to be stored and transmitted.
Solution Approach 2:
By transforming the encoded parameter from the full angle value to the angle difference (residual), the patent maintains the necessary precision for laser angle specification while reducing the data quantity. The prediction model ensures that the reconstructed angle maintains adequate precision for the application.
Data Source
AI summary
A method comprises obtaining a first laser angle; obtaining a second laser angle; obtaining a laser angle difference for a third laser angle; determining a predicted value based on the first laser angle and the second laser angle; and determining the third laser angle based on the predicted value and the laser angle difference for the third laser angle.