Cross-Domain Compression Networks for Low-Latency Lossless Coding
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Solution Overview
Problem
Existing data compression methods, particularly in satellite telemetry and command systems, lack efficient and low-latency lossless compression solutions that maintain original information integrity.
Innovation Solution
A system and method utilizing a cross-domain knowledge transfer in federated compression networks, incorporating neural networks and probability prediction driven arithmetic coding with a long short-term memory system, to achieve fast and efficient lossless data compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional compression algorithms are used, then compression is achieved, but information loss occurs and latency increases
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with a neural network-based learning system. The compression network and decompression network use machine learning models to achieve lossless compression, substituting conventional algorithmic approaches with intelligent systems that can adapt to data patterns and maintain information integrity while reducing latency.
Solution Approach 2:
The patent dynamically adjusts compression parameters based on data characteristics. The neural networks learn optimal compression strategies by analyzing data patterns and adjusting their internal parameters (weights and biases) during training, enabling adaptive compression that maintains quality while optimizing speed for different data types.
2Loss of information
If compression ratio is increased, then more data is compressed, but information loss increases
Solution Approach 1:
The patent replaces fixed mechanical compression algorithms with flexible neural network systems that can adjust their compression approach based on data characteristics. The learning-based approach enables the system to achieve high compression ratios while preserving information by adapting to the specific patterns and structures in the input data.
Solution Approach 2:
The compression system is made dynamic through neural networks that can adapt their behavior based on input data characteristics. The networks learn to dynamically adjust compression strategies, allocating more resources to preserve important information while compressing less critical data more aggressively, thereby balancing information preservation with compression efficiency.
Data Source
AI summary
A system and method for cross-domain knowledge transfer in federated compression networks. The system enables efficient lossless data compression across diverse data types by intelligently sharing compression strategies between domains. A cross-domain knowledge transfer system identifies relationships between different data domains, adapts compression parameters accordingly, and optimizes learning processes to maximize knowledge reuse. The architecture may include a knowledge repository for storing domain features and compression patterns, domain mapping components that identify similarities, and transfer learning optimization that enables efficient adaptation with minimal examples. This approach significantly accelerates model training for new domains while improving compression performance. Applications include satellite telemetry systems where efficient compression is critical for transmitting large information sets between distant locations. The system may employ probability prediction driven arithmetic coding paired with long short-term memory networks, enhanced by cross-domain knowledge sharing that adapts successful compression strategies from one domain to another while preserving domain-specific optimization.