Conditional Entropy Modeling for Adaptive Neural Data Compression
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Solution Overview
Problem
Conventional data compression methods often rely on static, predetermined entropy models that fail to capture the inherent spatial dependencies in input data, leading to suboptimal compression rates and efficiency in data transmission and storage.
Innovation Solution
A data compression system using neural networks to dynamically determine a conditional entropy model based on input data, incorporating a hyper-encoder neural network to generate a hyper-prior that characterizes the entropy model, and jointly training networks to optimize a rate-distortion performance measure, enabling adaptive compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static, predetermined entropy models are used for data compression, then the compression system is simple to implement, but the compression rate and efficiency are suboptimal due to inability to capture spatial dependencies in input data
Solution Approach 1:
The patent applies dynamics by transforming the static, predetermined entropy model into a dynamic, data-dependent entropy model. The entropy model is now conditioned on the input data through neural network processing, allowing it to adapt and capture spatial dependencies specific to each input. This dynamic approach enables the system to achieve higher compression rates by modeling the actual data characteristics rather than relying on fixed assumptions.
Solution Approach 2:
The patent changes the parameters of the entropy model from fixed predetermined values to data-dependent parameters generated by neural networks. The entropy model parameters (such as probability distributions) are dynamically determined based on the input data characteristics, allowing the system to adapt to different data types and patterns. This parameter change enables the entropy model to capture spatial dependencies and achieve optimal compression for each specific input.
2Loss of information
If data-dependent entropy models are used to capture spatial dependencies, then compression efficiency improves, but the complexity of determining the entropy model increases
Solution Approach 1:
The patent replaces the mechanical/manual process of entropy model determination with an automated neural network-based system. Instead of relying on complex manual analysis or predetermined models, the system uses trained neural networks to automatically learn and determine the entropy model parameters from the input data. This substitution of mechanical processes with intelligent automation reduces the perceived complexity while maintaining high information retention.
Solution Approach 2:
The patent applies preliminary action by pre-training the neural networks on large datasets before actual compression. The neural networks are trained in advance to learn the relationships between input data and optimal entropy model parameters. This preliminary training phase captures spatial dependencies and patterns, so that during actual compression, the system can quickly and efficiently determine the entropy model without complex real-time analysis, thus reducing operational complexity.
3Adaptability or versatility
If neural networks are used to dynamically determine entropy models, then adaptive compression is achieved, but the computational resources required increase
Solution Approach 1:
The patent applies preliminary action by performing the computationally intensive neural network training phase separately from the actual compression operation. The neural networks are trained in advance on large datasets to learn optimal entropy model determination. Once trained, the networks can quickly infer entropy models for new data with significantly reduced computational requirements. This separates the heavy computational burden from the operational compression phase, making adaptive compression more energy-efficient.
Solution Approach 2:
The patent uses copying by having the neural network learn and replicate the statistical patterns and spatial dependencies present in the training data. Instead of performing complex real-time analysis during compression, the network has already copied and stored the essential patterns during training. This allows the system to apply these learned patterns efficiently during actual compression, reducing computational energy requirements while maintaining high adaptability.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, a method comprises: processing data using an encoder neural network to generate a latent representation of the data; processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model; generating an entropy encoded representation of the latent representation of the entropy model; generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model; and determining a compressed representation of the data from the entropy encoded representations of: (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.