  Super-resolution (SR) has become a widely researched topic in recent years.
SR methods can improve overall image and video quality and create new
possibilities for further content analysis. But the SR mainstream focuses
primarily on increasing the naturalness of the resulting image despite
potentially losing context accuracy. Such methods may produce an incorrect
digit, character, face, or other structural object even though they otherwise
yield good visual quality. Incorrect detail restoration can cause errors when
detecting and identifying objects both manually and automatically. To analyze
the detail-restoration capabilities of image and video SR models, we developed
a benchmark based on our own video dataset, which contains complex patterns
that SR models generally fail to correctly restore. We assessed 32 recent SR
models using our benchmark and compared their ability to preserve scene
context. We also conducted a crowd-sourced comparison of restored details and
developed an objective assessment metric that outperforms other quality metrics
by correlation with subjective scores for this task. In conclusion, we provide
a deep analysis of benchmark results that yields insights for future SR-based
work.
