Real-time GenAI Solutions for Video Streaming in Low-bandwidth Settings

摘要

Surveillance systems such as bodycams and drones often operate under bandwidth constraints that limit video quality and degrade both human monitoring and AI-based analytics. Traditional compression techniques introduce artifacts that obscure critical details, especially in high-motion scenarios. We present a generative AI-powered video compression framework developed by Small Pixels, a spin-off of the University of Florence, designed to deliver Full HD video at significantly reduced bitrates. The system combines edge-side preprocessing for compression resilience with real-time receiver-side super-resolution, enabling up to 50% bandwidth savings while preserving perceptual quality and detection accuracy. Objective evaluations on EgoSeg and VisDrone datasets show +6.2 VMAF improvement and stable YOLOv11 detection performance with 30% less bitrate. Live trials in Singapore, within the Singapore Hatch-X Global Innovation Program, validated real-time operation with minimal latency, demonstrating clearer faces and motion in challenging conditions. The solution integrates seamlessly into existing infrastructures without hardware upgrades, offering a practical path to reliable, high-quality video streaming in bandwidth-limited environments.