  A large-scale video quality dataset called the VideoSet has been constructed
recently to measure human subjective experience of H.264 coded video in terms
of the just-noticeable-difference (JND). It measures the first three JND points
of 5-second video of resolution 1080p, 720p, 540p and 360p. Based on the
VideoSet, we propose a method to predict the satisfied-user-ratio (SUR) curves
using a machine learning framework. First, we partition a video clip into local
spatial-temporal segments and evaluate the quality of each segment using the
VMAF quality index. Then, we aggregate these local VMAF measures to derive a
global one. Finally, the masking effect is incorporated and the support vector
regression (SVR) is used to predict the SUR curves, from which the JND points
can be derived. Experimental results are given to demonstrate the performance
of the proposed SUR prediction method.
