Adaptive Neural Data Compression With Temporal Quality Control
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Solution Overview
Problem
Existing lossy compression methods lack flexibility in balancing compression efficiency and reconstruction quality, failing to adapt to dynamic data characteristics and application-specific needs, and do not effectively capture temporal dependencies in data.
Innovation Solution
A system and method employing a joint learning framework with a Vector Quantized Variational Autoencoder (VQ-VAE) and Multilayer Perceptron Long Short-Term Memory (MLP-LSTM) to dynamically adjust compression parameters, capturing temporal dependencies and optimizing compression performance.
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 patent implements dynamic adjustment of compression parameters based on temporal context. The system uses variable quantization steps and adaptive compression ratios that change over time based on the importance of temporal patterns, allowing the compression rate to be flexible rather than fixed. This enables the system to maintain high reconstruction quality when temporal patterns are critical while achieving higher compression ratios when they are less important.
Solution Approach 2:
The system dynamically changes compression parameters including quantization step sizes, compression ratios, and threshold values based on the analyzed temporal dependencies. By adjusting these parameters adaptively rather than using fixed values, the system can optimize the balance between compression efficiency and reconstruction quality for different data segments and temporal contexts.
2Device complexity
If fixed compression parameters are used, then device complexity is reduced, but adaptability to different data characteristics deteriorates
Solution Approach 1:
The compression system performs self-analysis of temporal dependencies in the input data and automatically adjusts its compression parameters based on this analysis. The system includes built-in temporal pattern detection and adaptive parameter selection mechanisms that enable it to adapt to different data characteristics without requiring external configuration or complex manual tuning, achieving high adaptability with moderate system complexity.
3Speed
If temporal dependencies are not captured, then processing speed is improved, but compression performance deteriorates
Solution Approach 1:
The patent segments the temporal data into meaningful units and processes them using specialized temporal modeling techniques. By dividing the data processing into discrete temporal segments that can be analyzed independently yet maintain contextual relationships, the system achieves efficient processing speed while capturing essential temporal dependencies for improved compression performance.
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.