Split Point Compression Profiles for AI Point Cloud Coding
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Solution Overview
Problem
Current video coding systems, including wavelet-based, object-based, and block-based systems, are inadequate for effective compression and processing of point clouds.
Innovation Solution
Implementing split point configurations with compression profiles, where endpoints negotiate and select split point profiles that match artificial intelligence application requirements, determining compression formats, and processing intermediate data accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current video coding systems (wavelet-based, object-based, block-based) are used for point cloud compression, then general video compression is achieved, but compression efficiency for point clouds is inadequate
Solution Approach 1:
The patent introduces split point configuration profiles that define specific parameter settings for different AI application tasks. These profiles adjust compression parameters (such as precision, data format, and processing depth) to optimize the balance between compression efficiency and AI task performance requirements, resolving the inadequacy of general video coding systems for point cloud-specific applications.
Solution Approach 2:
The system dynamically selects and negotiates split point profiles between endpoints based on real-time AI application requirements. This dynamic adaptation allows the compression system to adjust its characteristics according to specific task demands, improving both compression efficiency and adequacy for different point cloud processing scenarios.
2Productivity
If split point profiles are negotiated and selected for specific AI tasks, then compression is optimized for AI applications, but system complexity increases
Solution Approach 1:
The patent pre-defines multiple split point configuration profiles with optimized parameters for different AI application types. These profiles are established in advance and stored in the system, eliminating the need for complex real-time optimization calculations. The negotiation process simply selects from these pre-configured options, reducing system complexity while maintaining AI task optimization.
Solution Approach 2:
Different split point profiles are tailored to specific AI application requirements (e.g., classification, detection, segmentation tasks). Each profile contains locally optimized parameters suited for its target application, allowing the system to achieve high AI processing efficiency without requiring a single complex universal configuration mechanism.
3Reliability
If compression formats are determined and intermediate data is processed according to split point profiles, then data transmission quality is improved, but processing time increases
Solution Approach 1:
The split point profiles define specific parameter settings for intermediate data processing, including compression format selection and data characteristic adjustments. By changing these parameters according to pre-defined profiles rather than performing complex real-time analysis, the system improves data transmission quality while minimizing additional processing time.
Data Source
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AI summary
Systems, methods, and instrumentalities are disclosed associated with split point configurations with compression profile. For example, endpoints may select and/or negotiate split point configuration information (e.g., profiles). Split point configuration profiles may include a split point data characteristic and/or related compression characteristics that may be performed with an associated compression profile. Endpoints (e.g., a first endpoint and a second endpoint) may compute split point data characteristics and/or related compression characteristics of different split point profiles, for example, to select the split point profile that matches the expected application task (e.g., artificial intelligence (Al)) take requirements.