  The just-noticeable-difference (JND) visual perception property has received
much attention in characterizing human subjective viewing experience of
compressed video. In this work, we quantify the JND-based video quality
assessment model using the satisfied user ratio (SUR) curve, and show that the
SUR model can be greatly simplified since the JND points of multiple subjects
for the same content in the VideoSet can be well modeled by the normal
distribution. Then, we design an SUR prediction method with video quality
degradation features and masking features and use them to predict the first,
second and the third JND points and their corresponding SUR curves. Finally, we
verify the performance of the proposed SUR prediction method with different
configurations on the VideoSet. The experimental results demonstrate that the
proposed SUR prediction method achieves good performance in various resolutions
with the mean absolute error (MAE) of the SUR smaller than 0.05 on average.
