Generative Video Frame Synthesis for Connection Interruptions
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Solution Overview
Problem
Existing solutions for handling temporal disturbances in video streams, such as those encountered in AR/VR applications, are inadequate for prolonged interruptions or degradations, leading to user experience issues due to video freezes and quality degradation.
Innovation Solution
A method and apparatus utilizing a generative model, specifically a variational autoencoder, to encode and decode video frames into latent vectors, allowing for the generation of synthetic frames even during significant connection interruptions, thereby maintaining video playback continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video stream processing is used, then video playback is simple and straightforward, but video freezes occur during connection interruptions causing poor user experience
Solution Approach 1:
A generative AI model is introduced as an intermediary component between the video stream and the display. When connection interruptions occur, the AI model generates synthetic video frames that bridge the gap between received frames, preventing playback freezes without requiring complex traditional video processing systems
Solution Approach 2:
Instead of relying on actual video data during interruptions, the system creates synthetic copies of video frames through the generative AI model. These generated frames replicate the visual content based on learned patterns from training data, maintaining playback continuity without requiring the original video stream
2Reliability
If generative AI models are used to generate synthetic video frames, then video playback continuity is maintained during connection interruptions, but computational resources and processing time are significantly increased
Solution Approach 1:
The generative AI model is pre-trained offline using extensive video data and computational resources. This preliminary training creates a neural network that can generate video frames efficiently during actual use without requiring real-time training, reducing the computational burden during connection interruptions
Solution Approach 2:
The system dynamically adjusts the level of AI generation based on connection conditions. During minor interruptions, the model generates frames at full resolution; during severe interruptions, it can operate at reduced computational intensity, optimizing the balance between playback continuity and energy consumption
3Ease of operation
If synthetic video frames are generated during connection degradation, then user experience quality is improved, but the complexity of video processing and system overhead increases
Solution Approach 1:
The generative AI model serves itself by automatically detecting connection quality and initiating frame generation without external intervention. The system monitors its own performance metrics and self-regulates when to switch between real and synthetic frames, eliminating the need for complex external control mechanisms
Data Source
AI summary
Embodiments disclosed herein relate to methods and apparatus for generating video frames when there is a change in the rate of received video data. In one embodiment there is provided a method of processing video data which comprises generating a video frame using received video data, encoding said video frame into a latent vector using an encoder part of a generative model, modifying the latent vector and decoding the modified latent vector using a decoder part of the generative model to generate a new video frame in response to determining a reduction in generating the video frames using the received video data.