Point Cloud Attribute Encoding with Hierarchical Tree Transforms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing point cloud attribute encoding and decoding methods fail to effectively utilize the correlation between child nodes, leading to poor performance in point cloud attribute compression.
Innovation Solution
A point cloud attribute encoding method that constructs an N-layer binary tree or K-D tree based on point cloud geometry, transforms attribute coefficients using Haar wavelet and discrete cosine transforms, and entropy encodes the results to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If nearest neighbor prediction is used for point cloud attribute compression, then the encoding process is simple, but the compression performance is poor due to failure to utilize attribute correlation between child nodes
Solution Approach 1:
The patent segments the point cloud attribute data into a hierarchical tree structure (octree or KD-tree) where child nodes represent subdivided spatial regions. This segmentation enables the application of transform coding to exploit correlations between parent and child nodes, thereby improving compression performance while maintaining manageable encoding complexity through hierarchical processing.
Solution Approach 2:
The patent introduces a hierarchical dimension by organizing point cloud attributes into multi-level tree structures. This dimensional transformation from flat nearest-neighbor prediction to hierarchical transform coding allows exploitation of inter-node correlations across different levels, significantly improving compression efficiency without substantially increasing encoding complexity.
2Loss of information
If hierarchical transform coding is applied to exploit attribute correlation, then compression performance improves, but encoding complexity increases
Solution Approach 1:
The patent divides the point cloud attribute data into a hierarchical tree structure with multiple levels, where each level processes a subset of nodes. This segmentation allows the complex transform coding to be applied in a distributed, level-by-level manner, improving compression performance while managing encoding complexity through modular hierarchical processing.
Solution Approach 2:
The patent performs preliminary organization of point cloud data into hierarchical tree structures (octree or KD-tree) before applying transform coding. This preliminary structuring enables efficient exploitation of attribute correlations between parent and child nodes, achieving superior compression performance while keeping the subsequent encoding process systematic and manageable.
Data Source
AI summary
A point cloud attribute encoding method, a decoding method, an encoding device and a decoding device are disclosed, the point cloud attribute encoding method including: constructing an N-layer binary tree by partitioning a target point cloud according to positions of points within the point cloud, N being an integer greater than 1; for a target node at layer P of the binary tree, obtaining child nodes of the target node, determining a first attribute coefficient and second attribute coefficients of the target node by transforming first attribute coefficients of the child nodes, P being an integer greater than or equal to 1 and less than or equal to N−1; using the first attribute coefficient of a root node and the second attribute coefficients of each target node in the binary tree as output coefficients of the point cloud attribute encoding method.