Point Cloud Decoding Indication for Attribute Processing Bottlenecks
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Solution Overview
Problem
The large amount of attribute information in point clouds leads to pressure during decoding, resulting in low decoding performance.
Innovation Solution
A point cloud processing method that includes generating and encoding decoding indication information for different types of data, such as point cloud frames and slices, to improve decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all attribute information of each point in the point cloud is encoded and transmitted, then the completeness of point cloud data is improved, but the decoding pressure increases and decoding performance deteriorates
Solution Approach 1:
The patent segments the point cloud data into different types (geometry information and attribute information) and further divides attribute information into different attribute types. By applying different encoding strategies to different segments, the patent reduces the overall decoding pressure while maintaining necessary data completeness. Specifically, not all attribute information is fully encoded, but only essential portions are transmitted to balance completeness and decoding performance.
Solution Approach 2:
The patent extracts and identifies essential attribute information that must be preserved for accurate point cloud representation. By separating essential attribute information from non-essential attribute information, the patent transmits only the extracted essential portions, thereby reducing decoding pressure while maintaining the necessary completeness for functional requirements.
2Measurement precision
If a large amount of attribute information is transmitted, then the accuracy of point cloud reconstruction is improved, but the transmission efficiency and decoding speed deteriorate
Solution Approach 1:
The patent applies different quality levels to different parts of the point cloud data. Essential attribute information is transmitted with high accuracy to ensure reconstruction precision where it matters most, while non-essential attribute information is either compressed or omitted. This local quality approach maintains accuracy for critical reconstruction aspects while improving overall decoding speed.
3Reliability
If comprehensive attribute information is encoded, then the fidelity of point cloud representation is improved, but the data volume and processing complexity increase
Solution Approach 1:
The patent applies asymmetric encoding strategies where different types of attribute information receive different levels of encoding detail. Essential attribute information is encoded with high fidelity to maintain representation reliability, while non-essential attribute information uses simplified or omitted encoding. This asymmetric approach maintains fidelity where needed while reducing overall processing complexity.
Data Source
AI summary
A method of point cloud processing is provided. In some examples, a point cloud code stream of a point cloud having a plurality of point cloud frames is obtained. The point cloud code stream includes decoding indication information that provides decoding information for different element levels of the point cloud, the different element levels of the point cloud include at least one of: a point cloud frame level and a point cloud slice level. The decoding indication information includes a decoding of a cross-type attribute prediction parameter based on an attribute encoding order field. The point cloud code stream is decoded based on the decoding indication information. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.