Point Cloud Media Compression Units for Selective Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Point cloud media encoding and decoding face challenges due to large data volume and redundancy, necessitating improved flexibility and efficiency in encoding and decoding processes.
Innovation Solution
A method and apparatus for encoding and decoding point cloud media that utilize compression units with type-specific headers and slices, allowing selective decoding based on type information to enhance flexibility and reduce computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point cloud media is encoded and transmitted in traditional formats, then complete point cloud data can be transmitted, but the transmitted data volume is large and contains data redundancy
Solution Approach 1:
The point cloud data is segmented into different types of compression units (geometry header, attribute header, geometry slice, attribute slice) organized in a hierarchical structure. This segmentation allows selective transmission and decoding of specific components based on application needs, reducing overall data volume while maintaining essential information.
Solution Approach 2:
Different parts of the point cloud data are treated with different quality levels. The type information field enables the decoder to determine which compression units to decode based on local requirements, allowing high-quality reconstruction of critical regions while using lower quality or skipping less critical regions.
2Loss of information
If all compression units are decoded to ensure complete point cloud reconstruction, then data completeness is maintained, but computational resources and decoding time increase
Solution Approach 1:
The decoder performs partial decoding by selectively processing only the necessary compression units based on type information. Instead of decoding all compression units, the system decodes only those required for the current application scenario, significantly reducing decoding time while maintaining sufficient data completeness.
Solution Approach 2:
The decoding process is made dynamic and adaptive. The type information field enables real-time determination of which compression units to decode based on current application requirements, allowing the system to adjust decoding behavior dynamically rather than following a fixed decode-all approach.
3Adaptability or versatility
If traditional encoding methods are used, then point cloud data can be transmitted, but flexibility in encoding and decoding is limited
Solution Approach 1:
The encoding structure is designed to be universal and multi-functional. The type information field serves multiple purposes: it identifies compression unit types, guides selective decoding, and enables different application scenarios to utilize the same encoding framework with varying levels of detail, enhancing overall system flexibility.
Data Source
AI summary
In a method for decoding point cloud media, point cloud media data including a plurality of point cloud samples encapsulated in one or more tracks is received. A compression unit of a point cloud sample in the plurality of point cloud samples is obtained. A media file data box of the point cloud sample includes type information that indicates a type of the compression unit. The type of the compression unit is one of a geometry header indicating a parameter set of geometry information, an attribute header indicating a parameter set of attribute information, a geometry slice indicating point cloud slice data of the geometry information, and an attribute slice indicating point cloud slice data of the attribute information. Whether the compression unit is to be decoded is determined according to the type information. The compression unit is decoded to obtain point cloud data based on the determination.