Split VAE Video Compression for Edge-Cloud Data Restoration

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Solution Overview

Problem

Existing video compression and decompression systems face challenges in efficiently adapting to varying computational resources and network conditions across edge and cloud infrastructure, leading to suboptimal data quality and resource utilization.

Innovation Solution

A distributed system utilizing a split variational autoencoder architecture across edge and cloud computing devices, with resource monitoring, adaptive compression parameters, and a neural upsampler to enhance decompressed outputs, while maintaining high-quality reconstruction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If video compression is performed using traditional methods, then data transmission efficiency is improved, but data quality and reconstruction accuracy deteriorate

Engineering Contradiction:

Improvedata transmission efficiencyVSAvoiddata quality

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent replaces traditional mechanical compression algorithms with a neural network-based autoencoder system. The encoder compresses video data through learned representations in latent space, while the decoder reconstructs the original data, achieving both high compression efficiency and quality through intelligent pattern recognition rather than fixed mathematical transformations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system dynamically adjusts compression parameters including latent space dimensionality, number of encoder/decoder layers, and training loss weights based on available computing resources and network conditions. This allows the compression quality to be optimized for different operational scenarios while maintaining efficient transmission.

Inventive Principle:

Principle #35Parameter changes

2Manufacturing precision

If deep learning models are trained to improve data restoration quality, then reconstruction accuracy is improved, but training time and computational resources increase

Engineering Contradiction:

Improvereconstruction accuracyVSAvoidtraining time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The autoencoder is divided into multiple trainable layers (encoder layers, latent space, decoder layers) that can be trained incrementally. The system uses progressive training where simpler layers are trained first, then more complex layers are added and trained on top, reducing overall training time while achieving high reconstruction accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary training on simplified versions of the autoencoder architecture before full training. Pre-trained weights and features are transferred to the final model, significantly reducing the training time required to achieve high reconstruction accuracy on the complete system.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If multi-layer autoencoders are used to learn hierarchical representations, then feature learning capability is improved, but system complexity increases

Engineering Contradiction:

Improvefeature learning capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

Different layers of the autoencoder are specialized for different aspects of feature learning. Early encoder layers capture basic local features, while deeper layers capture more abstract global patterns. The decoder layers are specialized for reconstruction tasks, creating local optimization in each layer that collectively achieves high overall performance without excessive complexity.

Inventive Principle:

Principle #3Local quality

4Productivity

If compression parameters are dynamically adjusted based on resources, then resource utilization is improved, but system adaptability requirements increase

Engineering Contradiction:

Improveresource utilizationVSAvoidsystem adaptability requirements

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system continuously monitors available computing resources and network conditions, then feeds this information back to adjust compression parameters in real-time. The resource monitor tracks CPU usage, memory availability, and network bandwidth, dynamically modifying the autoencoder configuration to optimize performance under current conditions without requiring complex manual intervention.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250254024A1Adaptive Video Compression with Enhanced Data Restoration

Publication Date: 2025.08.07 ATOMBEAM TECH INC

AI summary

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system preprocesses raw data at edge computing devices, compresses the data into latent space vectors using distributed encoders within a variational autoencoder spanning edge and cloud components, decompresses the vectors using decoders, and processes them through a resource-aware neural upsampler to generate enhanced reconstructed outputs. The system dynamically adapts compression based on available computing resources and network conditions, while enabling secure distributed processing through homomorphic operations on compressed data. Edge-cloud coordination layers manage data flow, compression parameters, and workload distribution, while maintaining system reliability through intelligent failover handling and resource optimization.