3D Point Cloud Encoding With Region-Specific Quantization
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Solution Overview
Problem
Existing three-dimensional data encoding methods struggle with efficient compression and transmission of massive point cloud data, necessitating improved encoding techniques to manage the volume of data effectively.
Innovation Solution
A method involving calculating coefficient values from attribute information of three-dimensional points, quantizing these values, and generating a bitstream by classifying attribute information into groups within rectangular parallelepipeds, using quantization parameters for appropriate encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud data is compressed using existing encoding methods, then data volume is reduced for accumulation and transmission, but encoding efficiency and compression performance are insufficient
Solution Approach 1:
The patent divides the three-dimensional space into multiple rectangular parallelepipeds (voxels), and further segments each voxel into multiple quantization units. This segmentation allows different quantization parameters to be applied to different regions, improving compression efficiency while maintaining encoding performance.
Solution Approach 2:
The patent applies different quantization parameters to different quantization units within each voxel based on local characteristics. This local quality approach enables optimized compression for each region, improving overall encoding efficiency while maintaining data quality where needed.
2Device complexity
If quantization parameters are uniformly applied to all three-dimensional spaces, then processing is simplified, but encoding performance deteriorates for specific regions
Solution Approach 1:
The patent segments the three-dimensional space into multiple rectangular parallelepipeds and further into quantization units, allowing different quantization parameters to be applied to different regions. This segmentation enables optimized encoding performance for each region while maintaining manageable processing complexity through systematic organization.
Solution Approach 2:
The patent dynamically selects quantization parameters based on the characteristics of each quantization unit and voxel. This dynamic approach allows the system to adapt to different regions' requirements, improving encoding performance without requiring overly complex processing, as the parameter selection is based on predefined characteristics.
Data Source
AI summary
A three-dimensional data encoding method includes: calculating coefficient values from items of attribute information of three-dimensional points; quantizing the coefficient values to generate quantization values; and generating a bitstream including the quantization values. One or more items of attribute information are classified, for each of three-dimensional spaces, into one of groups, the three-dimensional spaces (i) being included in a plurality of three-dimensional spaces, and (ii) including, among the three-dimensional points, three-dimensional points including the one or more items of attribute information. In the quantizing, the coefficient values are quantized using a predetermined quantization parameter or one or more quantization parameters for one or more groups, the one or more groups being included in the groups and including one or more items of attribute information used to calculate the coefficient values, the one or more items of attribute information being included in the items of attribute information.