Neural Data Compression With Multi-Pass Latent Bitstreams
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Solution Overview
Problem
Current data compression technologies face challenges in efficiently compressing and decompressing data while maintaining quality, especially in large-scale data centers and mobile devices, where storage and bandwidth are limited.
Innovation Solution
The use of a multi-pass neural network system that employs machine learning techniques, such as autoencoders, variational autoencoders, and Generative Adversarial Networks, to identify patterns in data and generate efficient compressed representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data compression techniques are used, then storage space is reduced, but compression ratio and quality maintenance are insufficient
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with neural network-based machine learning systems. The neural networks learn complex patterns and representations of data, enabling superior compression ratios while maintaining quality. The encoder-decoder architecture with latent space representation captures underlying data structures that traditional methods miss, resolving the contradiction between compression efficiency and quality preservation.
Solution Approach 2:
The patent transforms data into different parameter spaces through neural network encoding. By mapping data to latent representations with different dimensionalities and characteristics, the system achieves efficient compression. The ability to adjust latent space dimensions and use different neural network architectures allows optimization of both compression ratio and reconstruction quality, addressing the quality-compression ratio tradeoff.
2Loss of energy
If data is compressed for efficient storage and transmission, then bandwidth and storage costs are reduced, but compression and decompression time increase
Solution Approach 1:
The patent employs pre-training of neural network models on large datasets before actual compression tasks. This preliminary learning phase enables the networks to quickly compress new data without requiring extensive processing time during actual use. The pre-learned representations and features are reused, significantly reducing real-time compression and decompression time while maintaining efficient bandwidth utilization.
3Productivity
If complex neural network models are used for pattern recognition, then compression efficiency is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent divides the compression system into separate encoder and decoder neural networks, each with specific functions. The encoder compresses data to latent representations while the decoder reconstructs the original data. This segmentation allows independent optimization of each component and enables deployment on devices with varying computational capabilities, reducing overall system complexity while maintaining high compression efficiency.
Data Source
AI summary
For compressing data, preprocessing operations are performed on raw input data. A discrete cosine transform is performed on the preprocessed data, and multiple subbands are created, where each subband represents a particular range of frequencies. The subbands are organized into multiple groups, where the multiple groups comprise a first low frequency group, a second low frequency group, and a high frequency group. A latent space representation is generated corresponding to each of the multiple groups of subbands. A first bitstream is created based on the latent space representation, and an alternate representation of the latent space is used for creating a second bitstream, enabling multiple-pass techniques for data compression. The multiple bitstreams may be multiplexed to form a combined bitstream for storage and/or transmission purposes.