Adaptive Transform Order for Point Cloud Coding Redundancy
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Solution Overview
Problem
In Geometry-based Point Cloud Compression (G-PCC), the Region Adaptive Hierarchical Transform (RAHT) method suffers from large information redundancy due to a fixed transform order, leading to poor coding efficiency for point clouds with varying spatial distributions.
Innovation Solution
Determine a transform order based on the sum of each coordinate of the normal vector or the projection area of two-dimensional planes in the encoding point cloud, prioritizing transform directions with significant features to reduce redundancy and improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed transform order is used in RAHT transform, then the transform process is simple and fast, but large information redundancy occurs leading to poor coding efficiency
Solution Approach 1:
The patent applies the dynamics principle by making the transform order adaptive rather than fixed. The system dynamically determines the transform order based on the spatial distribution characteristics of the point cloud data, allowing the encoding process to adapt to different data patterns and reduce information redundancy while maintaining coding efficiency.
Solution Approach 2:
The patent changes the parameter of transform order from a fixed value to a variable determined by spatial distribution analysis. By calculating spatial distribution parameters and using them to select or determine the transform order, the system optimizes the balance between coding efficiency and information redundancy reduction.
2Productivity
If a fixed transform order is used in RAHT transform, then the encoding process is straightforward, but coding performance deteriorates for point clouds with varying spatial distributions
Solution Approach 1:
The patent changes the transform order parameter based on spatial distribution characteristics. By introducing spatial distribution parameters and using them to determine the transform order, the system achieves adaptability to different point cloud configurations while maintaining a relatively straightforward encoding process.
Solution Approach 2:
The system implements feedback by analyzing the spatial distribution of point cloud data and using this information to adjust the transform order. This feedback mechanism allows the encoding process to adapt to the specific characteristics of the input data, improving coding performance across varying spatial distributions.
Data Source
AI summary
Provided by the implementations of the present disclosure are a transform method, a coder, a decoder and a computer readable storage medium. The transform method includes: determining normal vectors of encoding points in an encoding point cloud; analyzing the sum total of all coordinates of the normal vectors on the basis of the normal vectors of the encoding points; and determining a transform sequence on the basis of the sum total of all coordinates of the normal vectors.