Online Learning-based Video Reconstruction for Adaptive Bitrate Streaming
Temporal reconstruction of degraded frames in poor channel conditions
Developed online learning-based methods to reconstruct video frames during poor channel conditions in adaptive bitrate streaming. The approach leverages the inherent temporal self-similarity in video sequences to recover frames that suffer from heavy compression and downsampling.
Problem Statement: In adaptive bitrate video streaming, channel degradation causes:
- Increased compression artifacts
- Spatial downsampling
- Frame quality reduction
Solution Approach:
- Temporal self-similarity analysis across video frames
- Online learning framework for frame reconstruction
- Exploitation of correlation patterns between good and degraded frames
Applications:
- Streaming services (Netflix, Prime Video, YouTube)
- Mobile video delivery
- Real-time communication platforms