Point Cloud Coding Using Nonlinear Mapping Function
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Solution Overview
Problem
Existing point cloud coding methods struggle with efficiently compressing attribute information due to its non-uniform distribution, which affects coding efficiency.
Innovation Solution
A point cloud coding apparatus and method that generate a piecewise linear function as a nonlinear mapping function based on the distribution of attribute values, mapping these values to a limited range to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a linear mapping function is used to map sensor data to a compressed range, then coding efficiency is improved for uniformly distributed data, but coding efficiency deteriorates for non-uniformly distributed attribute information
Solution Approach 1:
The patent divides the attribute information range into multiple segments or intervals based on the distribution characteristics. Different linear mapping functions are applied to different segments, allowing each segment to be optimized for its specific distribution pattern. This segmentation approach enables the system to handle non-uniformly distributed data effectively while maintaining the simplicity and efficiency of linear mapping within each segment.
Solution Approach 2:
The patent changes the mapping parameters dynamically based on the distribution statistics of the attribute information. By analyzing the minimum and maximum values and distribution patterns, the system adjusts the mapping function parameters (such as slope and intercept) to optimize compression for the specific data characteristics. This parameter adaptation allows the same mapping framework to efficiently handle both uniform and non-uniform distributions.
2Productivity
If data is mapped to a smaller range for compression, then loss of information increases, but coding efficiency improves
Solution Approach 1:
The patent performs preliminary analysis of the attribute information distribution before applying the mapping function. By pre-calculating the minimum and maximum values and understanding the distribution pattern, the system can design an optimized mapping function that minimizes information loss while achieving effective compression. This preliminary action ensures that the mapping is tailored to the specific data characteristics rather than applying a generic compression approach.
Solution Approach 2:
The patent incorporates feedback mechanisms where the mapping function is refined based on the actual distribution characteristics observed in the data. The system uses the statistical properties (minimum, maximum, and distribution pattern) as feedback to adjust and optimize the mapping parameters, ensuring that the compression process preserves as much meaningful information as possible while achieving high compression efficiency.
Data Source
AI summary
The present disclosure relates to a point cloud coding device and a method using a mapping function. The point cloud coding device and method compress attribute values of a point cloud by generating a piecewise linear function that is a nonlinear mapping function based on a distribution of the attribute values. The point cloud coding device and method map the attribute values to values within a limited range by utilizing the nonlinear mapping function.