If you have ever tried to run a desktop-grade super-resolution model on an embedded board, you know the pain: fans spin, latency spikes, and the battery wilts. Our lightweight device-side video super-resolution effort set out to change that. We distilled a model with fewer than 100K parameters that can upscale SD, HD, or full HD footage to 4K in real time, all while staying inside the thermal and memory envelope of consumer electronics.
What we built
- Tiny-yet-faithful network. We rebuilt the backbone with efficient convolutions and context-aware attention so the model keeps texture fidelity despite the aggressive pruning and quantization needed for a 100K parameter budget.
- Real-time video flow. Every frame has to pass through feature extraction, temporal alignment, and upsampling before the next vsync. We reworked the scheduler so 4× spatial magnification still clears 30 fps on TV-grade SoCs.
- Deployment-proven. The pipeline already ships inside an unnamed international television product, so we obsess over OTA safety switches, metrics for temporal flicker, and graceful fallbacks to native resolution.
Why it was hard
Balancing fidelity and compute is always a high-wire act. Compress too much and hair strands, foliage, and night scenes smear away. Optimize only for PSNR and you miss your latency target. We leaned on neural architecture search plus plenty of human-in-the-loop tuning to stop the network from hallucinating detail or stuttering during fast pans.
Where it lands
- Consumer screens. TVs, tablets, and handheld consoles can finally upscale nostalgia-era footage without cloud help.
- Security stacks. Low-bandwidth surveillance feeds gain license-plate clarity only when something interesting happens, keeping storage costs flat.
What’s next
We are exploring content-aware throttling so the device can dynamically choose between speed and sharpness, plus a plug-in API that lets OEMs combine our upscaler with their HDR or motion-smoothing modules without duplicate buffers.
