  The great variations of videographic skills, camera designs, compression and
processing protocols, and displays lead to an enormous variety of video
impairments. Current no-reference (NR) video quality models are unable to
handle this diversity of distortions. This is true in part because available
video quality assessment databases contain very limited content, fixed
resolutions, were captured using a small number of camera devices by a few
videographers and have been subjected to a modest number of distortions. As
such, these databases fail to adequately represent real world videos, which
contain very different kinds of content obtained under highly diverse imaging
conditions and are subject to authentic, often commingled distortions that are
impossible to simulate. As a result, NR video quality predictors tested on
real-world video data often perform poorly. Towards advancing NR video quality
prediction, we constructed a large-scale video quality assessment database
containing 585 videos of unique content, captured by a large number of users,
with wide ranges of levels of complex, authentic distortions. We collected a
large number of subjective video quality scores via crowdsourcing. A total of
4776 unique participants took part in the study, yielding more than 205000
opinion scores, resulting in an average of 240 recorded human opinions per
video. We demonstrate the value of the new resource, which we call the LIVE
Video Quality Challenge Database (LIVE-VQC), by conducting a comparison of
leading NR video quality predictors on it. This study is the largest video
quality assessment study ever conducted along several key dimensions: number of
unique contents, capture devices, distortion types and combinations of
distortions, study participants, and recorded subjective scores. The database
is available for download on this link:
http://live.ece.utexas.edu/research/LIVEVQC/index.html .
