Point Cloud Attribute Coding Using Adaptive Dead Zone Sizing
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Solution Overview
Problem
Existing point cloud coding methods do not effectively utilize the characteristics of attribute values to improve coding efficiency, particularly in the encoding and decoding processes.
Innovation Solution
Adaptive dead zone quantization scheme is employed to adjust the size of the dead zone based on the impact of various characteristics of points, such as LOD, prediction mode, and index of point groups, to enhance the coding efficiency of point cloud coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed dead zone quantization scheme is used, then the coding process is simple, but the coding efficiency is poor due to inability to adapt to varying point characteristics
Solution Approach 1:
The patent implements dynamic adaptation of the dead zone quantization scheme by adjusting the dead zone size based on point characteristics. The system dynamically selects between different dead zone sizes (first dead zone size for points with high prediction impact, second dead zone size for points with low prediction impact) based on the predicted attribute value and point group index, thereby optimizing coding efficiency without requiring a completely complex system redesign.
Solution Approach 2:
The patent changes the parameter of dead zone size based on point characteristics. Specifically, it modifies the dead zone quantization parameter adaptively by selecting different dead zone sizes according to the point group index and predicted attribute value, allowing the system to optimize compression performance for different types of points while maintaining a relatively simple overall framework.
2Reliability
If a uniform quantization scheme is used, then the implementation is straightforward, but the prediction performance deteriorates due to lack of adaptation to point characteristics
Solution Approach 1:
The patent applies different quantization characteristics to different parts of the data based on point characteristics. It divides points into different groups and applies different dead zone sizes to different groups based on their predicted attribute values and group indices, ensuring that points with high prediction impact receive more precise quantization while maintaining overall system simplicity.
Solution Approach 2:
The patent adaptively changes the dead zone parameter based on local point characteristics. By modifying the dead zone size according to the point group index and predicted attribute value, the system improves prediction performance for critical points while avoiding excessive complexity in the overall quantization framework.
3Loss of substance
If the dead zone size is increased, then the data compression ratio improves, but the quantization precision decreases
Solution Approach 1:
The patent applies different dead zone sizes to different parts of the data based on their importance. Points with high prediction impact (certain point groups) use a smaller dead zone size to maintain precision, while points with low prediction impact (other point groups) use a larger dead zone size to improve compression ratio, thereby achieving local optimization of both precision and compression.
Solution Approach 2:
The patent adaptively changes the dead zone parameter based on point characteristics to balance compression and precision. By selecting different dead zone sizes according to the point group index and predicted attribute value, the system optimizes the trade-off between compression ratio and quantization precision for different types of points.
Data Source
AI summary
A point cloud coding device and a method using a dead zone quantization scheme adaptively, when encoding attribute values of points in a point cloud, are disclosed. The point cloud coding device and method adjust the size of dead zones based on the impact of various characteristics of the points on the prediction performance with the attribute values.