  A new methodology to measure coded image/video quality using the
just-noticeable-difference (JND) idea was proposed. Several small JND-based
image/video quality datasets were released by the Media Communications Lab at
the University of Southern California. In this work, we present an effort to
build a large-scale JND-based coded video quality dataset. The dataset consists
of 220 5-second sequences in four resolutions (i.e., $1920 \times 1080$, $1280
\times 720$, $960 \times 540$ and $640 \times 360$). For each of the 880 video
clips, we encode it using the H.264 codec with $QP=1, \cdots, 51$ and measure
the first three JND points with 30+ subjects. The dataset is called the
"VideoSet", which is an acronym for "Video Subject Evaluation Test (SET)". This
work describes the subjective test procedure, detection and removal of outlying
measured data, and the properties of collected JND data. Finally, the
significance and implications of the VideoSet to future video coding research
and standardization efforts are pointed out. All source/coded video clips as
well as measured JND data included in the VideoSet are available to the public
in the IEEE DataPort.
