Supremo: Cloud-Assisted Low-Latency Super-Resolution in Mobile Devices
Juheon Yi, Seongwon Kim, Joongheon Kim, Sunghyun Choi · IEEE Transactions on Mobile Computing · 2020
We present${\sf Supremo}$, 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${\sf Supremo}$to video applications by formulating a dynamic optimal control algorithm to design${\sf Supremo-Opt}$, which aims to maximize the impact of SR while satisfying latency and resource constraints under practical network conditions.${\sf Supremo}$upscales 360p image to 1080p in 122 ms, which is 43.68× faster than on-device GPU execution. Compared to cloud offloading-based solutions,${\sf Supremo}$reduces wireless network bandwidth consumption and end-to-end latency by 15.23× and 4.85× 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,${\sf Supremo-Opt}$guarantees robust performance in practical scenarios.