  Video super-resolution is one of the most popular tasks on mobile devices,
being widely used for an automatic improvement of low-bitrate and
low-resolution video streams. While numerous solutions have been proposed for
this problem, they are usually quite computationally demanding, demonstrating
low FPS rates and power efficiency on mobile devices. In this Mobile AI
challenge, we address this problem and propose the participants to design an
end-to-end real-time video super-resolution solution for mobile NPUs optimized
for low energy consumption. The participants were provided with the REDS
training dataset containing video sequences for a 4X video upscaling task. The
runtime and power efficiency of all models was evaluated on the powerful
MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of
accelerating floating-point and quantized neural networks. All proposed
solutions are fully compatible with the above NPU, demonstrating an up to 500
FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of
all models developed in the challenge is provided in this paper.
