  Real-world Super-Resolution (Real-SR) methods focus on dealing with diverse
real-world images and have attracted increasing attention in recent years. The
key idea is to use a complex and high-order degradation model to mimic
real-world degradations. Although they have achieved impressive results in
various scenarios, they are faced with the obstacle of evaluation. Currently,
these methods are only assessed by their average performance on a small set of
degradation cases randomly selected from a large space, which fails to provide
a comprehensive understanding of their overall performance and often yields
inconsistent and potentially misleading results. To overcome the limitation in
evaluation, we propose SEAL, a framework for systematic evaluation of real-SR.
In particular, we cluster the extensive degradation space to create a set of
representative degradation cases, which serves as a comprehensive test set.
Next, we propose a coarse-to-fine evaluation protocol to measure the
distributed and relative performance of real-SR methods on the test set. The
protocol incorporates two new metrics: acceptance rate (AR) and relative
performance ratio (RPR), derived from acceptance and excellence lines. Under
SEAL, we benchmark existing real-SR methods, obtain new observations and
insights into their performance, and develop a new strong baseline. We consider
SEAL as the first step towards creating a comprehensive real-SR evaluation
platform, which can promote the development of real-SR. The source code is
available at https://github.com/XPixelGroup/SEAL
