  This paper reviews the video extreme super-resolution challenge associated
with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned
video super-resolution (VSR) do not go beyond factor 4. Missing information can
be restored well in this region, especially in HR videos, where the
high-frequency content mostly consists of texture details. The task in this
challenge is to upscale videos with an extreme factor of 16, which results in
more serious degradations that also affect the structural integrity of the
videos. A single pixel in the low-resolution (LR) domain corresponds to 256
pixels in the high-resolution (HR) domain. Due to this massive information
loss, it is hard to accurately restore the missing information. Track 1 is set
up to gauge the state-of-the-art for such a demanding task, where fidelity to
the ground truth is measured by PSNR and SSIM. Perceptually higher quality can
be achieved in trade-off for fidelity by generating plausible high-frequency
content. Track 2 therefore aims at generating visually pleasing results, which
are ranked according to human perception, evaluated by a user study. In
contrast to single image super-resolution (SISR), VSR can benefit from
additional information in the temporal domain. However, this also imposes an
additional requirement, as the generated frames need to be consistent along
time.
