3D Point Cloud Attribute Coding with Layered Regional Parameters
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Solution Overview
Problem
Existing three-dimensional data encoding and decoding methods face inefficiencies in coding efficiency, particularly in handling large volumes of point cloud data.
Innovation Solution
A method and device for encoding and decoding three-dimensional data that utilizes parameters based on layers and regions to improve coding efficiency, including predetermined reference values and difference values for each layer and region, allowing for efficient encoding and decoding of attribute information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed using conventional encoding methods, then data transmission efficiency is improved, but coding efficiency deteriorates due to the massive amount of three-dimensional data
Solution Approach 1:
The three-dimensional space is divided into multiple layers, and within each layer, points are divided into multiple regions. This segmentation allows the encoding process to handle data in smaller, manageable units rather than processing the entire point cloud at once, thereby improving coding efficiency while maintaining data transmission efficiency.
Solution Approach 2:
Different parameters are applied to different regions within layers. Specifically, each region has its own parameter values (such as prediction parameters and threshold values) that are optimized for the local characteristics of that region. This local optimization improves coding efficiency by adapting to the specific properties of different spatial regions rather than using a uniform approach for the entire dataset.
2Ease of manufacture
If conventional encoding methods are used for three-dimensional data, then implementation simplicity is maintained, but coding efficiency deteriorates due to inability to handle large volumes of point cloud data effectively
Solution Approach 1:
The point cloud data is segmented into layers and regions, creating a hierarchical structure that is relatively simple to implement. The encoder processes each layer and region systematically, and the decoder reconstructs data following the same hierarchical structure. This segmented approach maintains implementation simplicity while dramatically improving coding efficiency for large volumes of three-dimensional data.
Data Source
AI summary
A three-dimensional data encoding method includes: encoding items of attribute information corresponding to respective three-dimensional points using a parameter; and generating a bitstream including the items of attribute information encoded and the parameter. Each of the items of attribute information belongs to one of at least one layer. Each of the three-dimensional points belongs to one of at least one region. The parameter is determined based on a layer to which an item of attribute information to be encoded in the encoding belongs, and a region to which a three-dimensional point having the item of attribute information to be encoded in the encoding belongs. The parameter included in the bitstream includes a predetermined reference value, a first difference value determined for each of the at least one layer, and a second difference value determined for each of the at least one region.