Video Stream Reconstruction for Low-Bandwidth Privacy-Preserving Conferencing
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Solution Overview
Problem
The increasing demand for videoconferencing has put pressure on infrastructure, leading to network congestion and limited uplink bandwidth, causing video quality degradation and privacy risks due to deep learning-based video super-resolution models containing user personal information.
Innovation Solution
A method and system that uses a pre-trained model fine-tuned during a video conference to convert low-resolution video streams into high-resolution streams within the service provider's infrastructure, ensuring privacy by destroying the conversion model after use, thus avoiding privacy leakage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep learning-based video super-resolution models are pre-stored with user personal information, then video quality can be improved, but privacy security deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training generic super-resolution models on diverse datasets before deployment. These pre-trained models are stored without user-specific information, and personalization occurs dynamically during video processing through lightweight adaptation techniques, thus avoiding privacy risks of storing pre-personalized models while maintaining video quality
Solution Approach 2:
The system introduces an intermediary mechanism where a generic pre-trained model serves as the base, and user-specific features are added dynamically through lightweight fine-tuning or feature injection during video processing. This intermediary approach separates the storage of generic models from user-specific personalization, maintaining both video quality and privacy security
2Loss of energy
If video resolution is reduced during network jamming, then bandwidth consumption is reduced, but video quality deteriorates
Solution Approach 1:
The system dynamically changes the resolution parameter of transmitted video based on network conditions. During network jamming, video is transmitted at lower resolution to reduce bandwidth consumption, while the pre-trained super-resolution model reconstructs high-quality video at the receiving end, thus maintaining video quality perception while actually reducing bandwidth usage
Solution Approach 2:
The system creates a high-quality copy of the low-resolution transmitted video through super-resolution reconstruction. The receiving端 generates a high-resolution version from the low-resolution input, effectively copying the visual quality experience without requiring high-resolution transmission, thus reducing bandwidth while maintaining perceived video quality
3Manufacturing precision
If conversion models are pre-stored for each user, then video reconstruction quality is improved, but device complexity and storage requirements increase
Solution Approach 1:
The system employs a universal pre-trained super-resolution model that can handle multiple users and scenarios. Instead of storing separate conversion models for each user, a single versatile model is deployed that adapts to different users through lightweight dynamic personalization techniques, thus reducing device complexity and storage requirements while maintaining reconstruction quality
Solution Approach 2:
The system enables self-service by allowing the pre-trained model to automatically adapt to user-specific characteristics during video processing without requiring pre-stored user-specific models. The model performs self-adjustment through lightweight fine-tuning or feature injection, eliminating the need for complex model storage infrastructure while maintaining reconstruction quality
Data Source
AI summary
Embodiments of the present disclosure relate to a method, a device, and a computer program product for video processing. A method in one embodiment comprises receiving a first portion of a video stream from a sending device, wherein frames in the video stream comprise images of a user, and the first portion has a first resolution. The method further comprises acquiring a pre-trained model associated with the user from a set of candidate models, and generating a conversion model by adjusting the pre-trained model using the first portion. The conversion model is used to convert a second portion of the video stream into a reconstructed video stream, the second portion having a resolution lower than the first resolution and the reconstructed video stream having a resolution higher than a resolution. Such an embodiment can provide high-quality video in a privacy-preserving manner, for example, as part of a secure video service.