Neural Video Memory Encoding for Compressed Secure Playback
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Solution Overview
Problem
Existing methods for storing and playing back sequence data, such as video, are inefficient in terms of memory and computing resources, and lack adequate privacy and security measures.
Innovation Solution
A neural network-based approach called Neural Video Memory Encoding (NVME) encodes video frames within its convolutional weights, allowing for efficient storage and playback, while incorporating a unique encryption mechanism using autoregressive noise generation and convolutional neural networks to ensure privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to store and playback sequence data, then video quality can be maintained, but significant memory and computing resources are consumed
Solution Approach 1:
The patent uses generative adversarial networks to create compressed representations of video data that can be replayed to reconstruct the original video sequence. Instead of storing complete video frames, the system stores compressed latent representations and uses GANs to generate the visual content during playback, significantly reducing memory requirements while maintaining video quality
Solution Approach 2:
The patent transforms video data from its original high-dimensional form into compressed latent space representations. By changing the parameter representation of video data through encoding networks, the system achieves efficient storage and retrieval while preserving the essential visual information needed for high-quality playback
2Ease of operation
If traditional methods are used to store and playback sequence data, then video data can be accessed, but computing resources are significantly consumed
Solution Approach 1:
The patent pre-trains generative adversarial networks to understand the statistical properties and patterns of video data. This preliminary training allows the system to efficiently encode and decode video sequences during playback without requiring intensive computing resources at runtime, as the heavy computational work has already been performed during the offline training phase
Solution Approach 2:
The system uses pre-trained generative models to create compressed copies of video data in latent space. During playback, these pre-trained models efficiently generate video frames from compressed representations, avoiding the need for intensive real-time processing of original video data and thus reducing computing resource consumption
3Ease of operation
If video data is stored in traditional formats, then data can be retrieved, but privacy and security are compromised
Solution Approach 1:
The patent introduces cryptographic keys and secure authentication mechanisms as intermediaries between the stored video data and the retrieval process. The video data is encrypted and stored in compressed form, and authorized users must provide valid authentication credentials to decrypt and retrieve the data, thereby preventing unauthorized access while maintaining ease of retrieval for legitimate users
Data Source
AI summary
Apparatuses, systems, and techniques to encode sequence data in one or more neural networks. In at least one embodiment, a video frame sequence is generated using a neural network to map noise frames to video frames.