Point Cloud Attribute Compression via Hierarchical Strip Partitioning
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Solution Overview
Problem
Existing point cloud attribute compression methods face challenges in balancing compression performance with computational complexity, particularly in fully utilizing spatial correlation and efficiently handling large data volumes.
Innovation Solution
A point cloud attribute compression method based on hierarchical partitioning, where the point cloud is adaptively partitioned into a 'strip-macro-block-block' hierarchy using spatial position and color information, allowing for efficient coding and error management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If octree decomposition and graph transformation are used, then compression performance is improved, but operation complexity increases
Solution Approach 1:
The patent divides the point cloud into multiple strips based on spatial position and attribute continuity, creating a hierarchical structure (frame→strip→macro block→block). This segmentation allows independent processing of each strip, reducing overall operation complexity while maintaining compression performance through localized graph transformation.
Solution Approach 2:
The patent introduces a new dimension of organization by arranging points in strips along the y-axis and dividing them into macro blocks and blocks. This multi-dimensional hierarchical structure transforms the traditional single-level processing into a layered approach, reducing computational complexity at each level while preserving spatial and attribute correlations.
2Loss of information
If KD tree decomposition and graph transformation are used, then compression performance is greatly improved, but operation complexity remains high
Solution Approach 1:
The patent segments the point cloud into strips with continuous attribute information, then further divides each strip into macro blocks and blocks. This hierarchical segmentation reduces the scale of graph transformation operations compared to processing the entire point cloud at once, thereby reducing operation complexity while maintaining compression performance.
Solution Approach 2:
The patent dynamically determines the number of strips and the depth of hierarchical division based on the specific characteristics of the point cloud data. This adaptive approach optimizes the balance between compression performance and operation complexity for different data types, avoiding excessive computational overhead.
3Productivity
If hierarchical partitioning into strip-macro-block-block is used, then coding efficiency is improved and fault tolerance is enhanced, but device complexity increases
Solution Approach 1:
The patent implements hierarchical partitioning that divides the point cloud into strips, macro blocks, and blocks, enabling independent coding of each strip. This segmentation improves coding efficiency through localized processing and enhances fault tolerance, as errors in one strip do not propagate to others. The structured hierarchy provides a systematic framework that manages device complexity through organized data flow.
Solution Approach 2:
The patent applies different coding strategies to different hierarchical levels (strip, macro block, block) based on local characteristics of the point cloud data. This local quality approach allows optimization of coding parameters for specific regions, improving overall coding efficiency while managing complexity through localized rather than global processing.
Data Source
AI summary
Disclosed is a hierarchical division-based point cloud attribute compression method. For point cloud attribute information, a new hierarchical division based coding scheme is proposed, wherein a frame of point cloud is adaptively divided into a “stripe-macroblock-block” hierarchical structure according to the spatial position and color distribution of the point cloud, and stripes are coded independently from one another, increasing the coding efficiency, enhancing the fault tolerance of a system and improving the performance of point cloud attribute compression. The method comprises: (1) inputting a point cloud; (2) division of a k-dimension (KD) tree structure of the point cloud; (3) continuity analysis of point cloud attribute information; (4) stripe division of the point cloud; (5) division of macroblocks and coding blocks of the point cloud; and (6) intra-frame prediction, transformation, quantification and entropy coding based on a block structure.