  Despite the growing popularity of video super-resolution (VSR), there is
still no good way to assess the quality of the restored details in upscaled
frames. Some SR methods may produce the wrong digit or an entirely different
face. Whether a method's results are trustworthy depends on how well it
restores truthful details. Image super-resolution can use natural distributions
to produce a high-resolution image that is only somewhat similar to the real
one. VSR enables exploration of additional information in neighboring frames to
restore details from the original scene. The ERQA metric, which we propose in
this paper, aims to estimate a model's ability to restore real details using
VSR. On the assumption that edges are significant for detail and character
recognition, we chose edge fidelity as the foundation for this metric.
Experimental validation of our work is based on the MSU Video Super-Resolution
Benchmark, which includes the most difficult patterns for detail restoration
and verifies the fidelity of details from the original frame. Code for the
proposed metric is publicly available at
https://github.com/msu-video-group/ERQA.
