  With the popularity of mobile devices, e.g., smartphone and wearable devices,
lighter and faster model is crucial for the application of video super
resolution. However, most previous lightweight models tend to concentrate on
reducing lantency of model inference on desktop GPU, which may be not energy
efficient in current mobile devices. In this paper, we proposed Extreme
Low-Power Super Resolution (ELSR) network which only consumes a small amount of
energy in mobile devices. Pretraining and finetuning methods are applied to
boost the performance of the extremely tiny model. Extensive experiments show
that our method achieves a excellent balance between restoration quality and
power consumption. Finally, we achieve a competitive score of 90.9 with PSNR
27.34 dB and power 0.09 W/30FPS on the target MediaTek Dimensity 9000
plantform, ranking 1st place in the Mobile AI & AIM 2022 Real-Time Video
Super-Resolution Challenge.
