Joint-Learning Lossy Compression for Temporal Data Trade-Off Control
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Solution Overview
Problem
Existing lossy compression methods lack flexibility in balancing compression efficiency and reconstruction quality, particularly in capturing temporal dependencies, which is crucial for applications like satellite telemetry and command systems that require efficient data transmission.
Innovation Solution
A controllable lossy compression system using a joint learning framework combining Vector Quantized Variational Autoencoder (VQ-VAE) and Multilayer Perceptron Long Short-Term Memory (MLP-LSTM) to encode, model, and decode data, allowing for adjustable compression parameters to balance compression ratio and reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression techniques are used to achieve higher compression ratios, then compression efficiency is improved, but reconstruction quality deteriorates
Solution Approach 1:
The system employs a controllable compression parameter that dynamically adjusts the degree of lossy compression, allowing the balance between compression efficiency and reconstruction quality to be optimized based on specific application requirements rather than being fixed
Solution Approach 2:
The invention changes the compression parameter to control the trade-off between compression ratio and reconstruction quality, enabling flexible adjustment of the compression strength to achieve desired performance levels
2Productivity
If existing lossy compression methods are used, then compression is achieved, but flexibility in balancing compression efficiency and reconstruction quality deteriorates
Solution Approach 1:
The system introduces a controllable compression parameter that enables dynamic adjustment of compression strength, providing flexibility to adapt to different application scenarios and data types without being constrained to fixed compression levels
3Device complexity
If temporal dependencies are not effectively captured, then compression processing is simpler, but compression performance deteriorates
Solution Approach 1:
The system performs preliminary temporal dependency modeling during the compression process, capturing temporal patterns before final compression to improve overall compression performance without adding excessive complexity to the main compression operation
Data Source
AI summary
A system and method for controllable lossy data compression employing a joint learning framework to efficiently compress and reconstruct input data while balancing compression ratio and reconstruction quality. The system comprises an encoding system, a temporal modeling system, and a decoding system, which are jointly optimized to minimize a combined loss function. The encoding system, such as a Vector Quantized Variational Autoencoder (VQ-VAE) compresses the input data into a compact representation, while introducing a controllable degree of lossy compression based on adjustable compression parameters. The temporal modeling system, such as a Multilayer Perceptron Long Short-Term Memory captures temporal dependencies in the compressed representation. The decoding system, such as a VQ-VAE decoder, reconstructs the input data from the compressed representation. By providing control over the trade-off between compression ratio and reconstruction quality, the system offers flexibility for diverse applications.