QuickSRNet: Plain Single-Image Super-Resolution Architecture for Faster Inference on Mobile Platforms
This work addresses the need for efficient upscaling solutions in applications like gaming and video playback on mobile platforms, representing an incremental improvement in speed and efficiency.
The paper tackled the challenge of enabling real-time deep learning-based super-resolution on mobile devices with compute, thermal, and power constraints by proposing QuickSRNet, which achieves 1080p outputs via 2x upscaling in 2.2 ms on a modern smartphone.
In this work, we present QuickSRNet, an efficient super-resolution architecture for real-time applications on mobile platforms. Super-resolution clarifies, sharpens, and upscales an image to higher resolution. Applications such as gaming and video playback along with the ever-improving display capabilities of TVs, smartphones, and VR headsets are driving the need for efficient upscaling solutions. While existing deep learning-based super-resolution approaches achieve impressive results in terms of visual quality, enabling real-time DL-based super-resolution on mobile devices with compute, thermal, and power constraints is challenging. To address these challenges, we propose QuickSRNet, a simple yet effective architecture that provides better accuracy-to-latency trade-offs than existing neural architectures for single-image super resolution. We present training tricks to speed up existing residual-based super-resolution architectures while maintaining robustness to quantization. Our proposed architecture produces 1080p outputs via 2x upscaling in 2.2 ms on a modern smartphone, making it ideal for high-fps real-time applications.