  Over the past decade, the online video industry has greatly expanded the
volume of visual data that is streamed and shared over the Internet. Moreover,
because of the increasing ease of video capture, many millions of consumers
create and upload large volumes of User-Generated-Content (UGC) videos. Unlike
streaming television or cinematic content produced by professional
videographers and cinemagraphers, UGC videos are most commonly captured by
naive users having limited skills and imperfect technique, and often are
afflicted by highly diverse and mixed in-capture distortions. These UGC videos
are then often uploaded for sharing onto cloud servers, where they further
compressed for storage and transmission. Our paper tackles the highly practical
problem of predicting the quality of compressed videos (perhaps during the
process of compression, to help guide it), with only (possibly severely)
distorted UGC videos as references. To address this problem, we have developed
a novel Video Quality Assessment (VQA) framework that we call 1stepVQA (to
distinguish it from two-step methods that we discuss). 1stepVQA overcomes
limitations of Full-Reference, Reduced-Reference and No-Reference VQA models by
exploiting the statistical regularities of both natural videos and distorted
videos. We show that 1stepVQA is able to more accurately predict the quality of
compressed videos, given imperfect reference videos. We also describe a new
dedicated video database which includes (typically distorted) UGC reference
videos, and a large number of compressed versions of them. We show that the
1stepVQA model outperforms other VQA models in this scenario. We are providing
the dedicated new database free of charge at
https://live.ece.utexas.edu/research/onestep/index.html
