Supremo: Cloud-Assisted Low-Latency Super-Resolution in Mobile Devices

Abstract

We present Supremo, a cloud-assisted system for low-latency image super-resolution (SR) in mobile devices. As SR is extremely compute-intensive, we first further optimize state-of-the-art DNN to reduce the inference latency. Furthermore, we design a mobile-cloud cooperative execution pipeline composed of specialized data compression algorithms to minimize end-to-end latency with minimal image quality degradation. Finally, we extend Supremo to video applications by formulating a dynamic optimal control algorithm to design Supremo-Opt, which aims to maximize the impact of SR while satisfying latency and resource constraints under practical network conditions. Supremo upscales 360p image to 1080p in 122 ms, which is 43.68x faster than on-device GPU execution. Compared to cloud offloading-based solutions, Supremo reduces wireless network bandwidth consumption and end-to-end latency by 15.23 x and 4.85x compared to baseline approach of sending and receiving whole images, and achieves 2.39 dB higher PSNR compared to using conventional JPEG to achieve similar data size compression. Furthermore, Supremo-Opt guarantees robust performance in practical scenarios.