Neural Conditional Entropy Models for Spatially Adaptive 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 utilizing 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 device complexity is reduced, but compression rate and efficiency deteriorate due to inability to capture spatial dependencies
Solution Approach 1:
The patent applies dynamics by transforming the static, predetermined entropy model into a dynamic, data-dependent entropy model. The system uses neural networks to generate entropy model parameters adaptively based on the input data characteristics, allowing the compression system to adjust to different data patterns and achieve higher compression rates while maintaining manageable complexity through learned representations.
Solution Approach 2:
The patent implements parameter changes by representing the entropy model through learnable parameters generated by neural networks. Instead of using fixed entropy model parameters, the system dynamically determines parameters such as probability distributions based on the input data, enabling the entropy model to adapt to different data characteristics and improve compression efficiency.
2Productivity
If data-dependent entropy models are used to improve compression rates, then compression efficiency increases, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural networks that generate entropy model parameters on large datasets. This preliminary training phase allows the system to learn effective representations and patterns, so that during actual compression operations, the system can efficiently generate appropriate entropy models without requiring excessive computational resources during the compression process itself.
3Adaptability or versatility
If neural networks are used to generate entropy model parameters, then adaptability to different data patterns improves, but training complexity increases
Solution Approach 1:
The patent implements self-service by designing a system where the neural networks automatically learn and adapt to different data patterns through self-supervised learning during the training phase. The system uses the data itself to generate training signals and learn appropriate entropy model parameters without requiring manual configuration or extensive external intervention, thereby achieving high adaptability while managing training complexity through automated learning processes.
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.