3D Point Cloud Encoding for Consecutive Zero Coefficient Compression
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Solution Overview
Problem
There is a demand for improving coding efficiency in three-dimensional data encoding processes, particularly in the context of point cloud data representation and compression.
Innovation Solution
The proposed method involves encoding three-dimensional data using a hierarchical structure that includes spatial prediction structures, where data is divided into spaces, volumes, and voxels, with predictions performed on a voxel-by-voxel basis, and utilizing meta-information for spatial and temporal predictions, along with separate encoding of static and dynamic objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If point cloud data is compressed using conventional encoding methods, then data transmission and storage become feasible, but coding efficiency remains insufficient due to the massive amount of point group data
Solution Approach 1:
The patent divides the point cloud data into multiple regions or blocks, and processes each region separately using region-adaptive hierarchical transform. This segmentation allows the encoding system to handle large datasets in manageable portions, improving overall coding efficiency while maintaining data integrity across the entire point cloud.
Solution Approach 2:
The patent applies different encoding strategies to different regions of the point cloud data based on local characteristics. By using region-adaptive hierarchical transform, the system tailors the compression approach to local data density and complexity, achieving better compression ratios in homogeneous regions while preserving detail in complex areas.
2Quantity of substance
If transform coefficients are encoded without considering consecutive zero values, then encoding process is simple, but code amount increases reducing compression efficiency
Solution Approach 1:
The patent extracts and separately encodes consecutive zero coefficients from the transform coefficient data. By identifying sequences of zero values and encoding them as a single unit rather than individual zeros, the system significantly reduces the code amount required to represent sparse regions of the point cloud data.
Solution Approach 2:
Instead of encoding each transform coefficient individually, the patent inverts the approach by encoding the pattern of zeros and non-zeros. This involves signaling the positions and lengths of zero sequences, which is more efficient when consecutive zeros are frequent, thereby reducing overall code amount.
Data Source
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AI summary
A three-dimensional data encoding method includes: calculating coefficient values from pieces of attribute information of three-dimensional points included in point cloud data (S6691); generating a second code sequence including first information and second information, the first information indicating a total number of zero coefficient values consecutive in a first code sequence in which the coefficient values are arranged, the second information indicating whether each of the coefficient values is 0, the zero coefficient values being included in the coefficient values and having a value of 0 (S6692); and generating a bitstream including the second code sequence (S6693). For example, each of the pieces of attribute information may include components, each of the coefficient values may include coefficient components corresponding to the components, the first information may indicate the total number of the consecutive coefficient values including the coefficient components all of which are 0, and the second information may indicate whether each of the coefficient components is 0.