Temporal Neural Data Compression with Adaptive Quality Balancing
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Solution Overview
Problem
Existing lossy compression methods lack adaptability to dynamic data characteristics and application-specific requirements, failing to effectively capture temporal dependencies and balance compression efficiency with reconstruction quality across diverse data types and applications.
Innovation Solution
A neural network-based compression system with temporal modeling and dynamic parameter adjustment that dynamically balances compression efficiency and reconstruction quality by using adjustable compression parameters and a distributed computing architecture, incorporating edge and central computing devices for efficient preprocessing, compression, and reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression techniques are used to achieve higher compression ratios, then data transmission efficiency is improved, but reconstruction quality deteriorates
Solution Approach 1:
The patent implements dynamic parameter adjustment where compression parameters are adaptively modified based on temporal dependencies and application-specific requirements. The system dynamically balances compression ratio and reconstruction quality by adjusting quantization parameters and processing depth according to data characteristics and real-time conditions, resolving the static trade-off between compression efficiency and quality.
Solution Approach 2:
The system changes compression parameters adaptively based on data type, application requirements, and temporal patterns. By modifying parameters such as quantization levels, processing precision, and model complexity dynamically, the system achieves variable compression ratios while maintaining acceptable reconstruction quality for different data characteristics and application scenarios.
2Device complexity
If fixed parameter compression methods are used, then system complexity is reduced, but adaptability to dynamic data characteristics deteriorates
Solution Approach 1:
The compression system performs self-adjustment by automatically analyzing data characteristics and adapting parameters without external intervention. The temporal modeling components and optimization algorithms enable the system to self-optimize compression settings based on observed patterns, data type, and application requirements, achieving high adaptability while maintaining reasonable system complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where compression performance, reconstruction quality, and data characteristics are continuously monitored. This feedback drives adaptive parameter adjustment and optimization, allowing the system to learn from performance metrics and improve its adaptability to different data types and application scenarios while managing complexity through iterative optimization.
3Speed
If temporal dependencies are not captured, then processing speed is improved, but compression performance deteriorates
Solution Approach 1:
The patent segments the compression process into distinct stages: temporal dependency analysis, parameter adjustment, and compression execution. By separating temporal modeling from the core compression operations and applying it selectively based on data characteristics, the system captures temporal dependencies for improved compression performance while minimizing their impact on overall processing speed through efficient algorithm design and selective application.
Data Source
AI summary
Data compression efficiently processes and reconstructs input data through adaptive optimization. The system receives input data, encodes it into a compressed representation, and modifies this representation by applying adjustable compression parameters. A temporal modeling component processes the modified representation to preserve sequential patterns and relationships. The system then generates reconstructed data and optimizes the entire process based on multiple criteria. By dynamically adjusting compression parameters and effectively modeling temporal dependencies, the system achieves superior compression performance and reconstruction quality across diverse data types and applications. The neural network-based approach enables adaptive performance optimization, balancing compression efficiency with high-fidelity reconstruction according to specific requirements.