  In real-world video super-resolution (VSR), videos suffer from in-the-wild
degradations and artifacts. VSR methods, especially recurrent ones, tend to
propagate artifacts over time in the real-world setting and are more vulnerable
than image super-resolution. This paper investigates the influence of artifacts
on commonly used covariance-based attention mechanisms in VSR. Comparing the
widely-used spatial attention, which computes covariance over space, versus the
channel attention, we observe that the latter is less sensitive to artifacts.
However, channel attention leads to feature redundancy, as evidenced by the
higher covariance among output channels. As such, we explore simple techniques
such as the squeeze-excite mechanism and covariance-based rescaling to counter
the effects of high channel covariance. Based on our findings, we propose
RealViformer. This channel-attention-based real-world VSR framework surpasses
state-of-the-art on two real-world VSR datasets with fewer parameters and
faster runtimes. The source code is available at
https://github.com/Yuehan717/RealViformer.
