Hierarchical Video Autoencoders for Compression and Infinite Zoom
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Solution Overview
Problem
Existing multi-layer autoencoder architectures focus primarily on data compression without fully exploiting correlations and patterns within the data, leading to suboptimal performance in data restoration and compression ratios.
Innovation Solution
A multi-layer autoencoder with a correlation layer that learns hierarchical representations and leverages correlations for enhanced data compression and restoration, utilizing hierarchical and Lorentzian autoencoders to maintain spatiotemporal relationships and enable infinite zoom capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multi-layer autoencoder is used for data compression, then compression ratio is improved, but data restoration quality deteriorates
Solution Approach 1:
The patent segments the compression and restoration process into multiple hierarchical layers, where each layer processes different levels of data abstraction. The encoder divides input data into hierarchical representations across multiple layers, and the decoder reconstructs data by progressively combining information from these segmented layers, allowing better preservation of restoration quality while achieving compression.
Solution Approach 2:
The patent implements nested hierarchical layers within the autoencoder architecture, where lower layers contain fine-grained details and higher layers contain abstract representations. Each layer is nested within the broader hierarchical structure, allowing the system to compress data by storing only essential hierarchical representations while reconstructing fine details through the nested layer structure.
2Loss of information
If correlation-based methods are added to exploit data patterns, then data restoration quality is improved, but device complexity increases
Solution Approach 1:
The patent merges correlation-based processing into the existing autoencoder framework by integrating correlation layers within the hierarchical structure. Instead of adding separate correlation-based systems, the patent combines correlation computation with the encoder-decoder layers, allowing the same hierarchical representations to serve both compression and correlation exploitation functions simultaneously.
Solution Approach 2:
The patent enables the autoencoder to perform self-correlation analysis within its hierarchical layers without requiring external correlation-based methods. The network learns to exploit correlations between different levels of hierarchical representation and between training samples through the correlation layers, allowing the system to improve restoration quality using its own internal structures rather than external complex mechanisms.
Data Source
AI summary
A system and method for compressing and restoring data using hierarchical autoencoders and Lorentzian autoencoders for video processing. For general data, the system employs hierarchical autoencoders operating at multiple scales. For video data, Lorentzian autoencoders preserve three-dimensional tensor structure where spatial and temporal relationships remain intact throughout compression and decompression. A correlation network, trained on cross-correlated data sets, enhances restoration by leveraging relationships between compressed representations, recovering information lost during compression. The Lorentzian approach enables advanced video features including temporal prediction and infinite zoom, where users can examine regions beyond original resolution with synthesized yet plausible details. This architecture achieves higher compression ratios while maintaining or improving data quality, particularly for video content where spatiotemporal coherence is critical, applicable to surveillance, medical imaging, entertainment, and remote sensing.