  Video and image quality assessment has long been projected as a regression
problem, which requires predicting a continuous quality score given an input
stimulus. However, recent efforts have shown that accurate quality score
regression on real-world user-generated content (UGC) is a very challenging
task. To make the problem more tractable, we propose two new methods - binary,
and ordinal classification - as alternatives to evaluate and compare
no-reference quality models at coarser levels. Moreover, the proposed new tasks
convey more practical meaning on perceptually optimized UGC transcoding, or for
preprocessing on media processing platforms. We conduct a comprehensive
benchmark experiment of popular no-reference quality models on recent
in-the-wild picture and video quality datasets, providing reliable baselines
for both evaluation methods to support further studies. We hope this work
promotes coarse-grained perceptual modeling and its applications to efficient
UGC processing.
