  The video quality assessment (VQA) technology has attracted a lot of
attention in recent years due to an increasing demand of video streaming
services. Existing VQA methods are designed to predict video quality in terms
of the mean opinion score (MOS) calibrated by humans in subjective experiments.
However, they cannot predict the satisfied user ratio (SUR) of an aggregated
viewer group. Furthermore, they provide little guidance to video coding
parameter selection, e.g. the Quantization Parameter (QP) of a set of
consecutive frames, in practical video streaming services. To overcome these
shortcomings, the just-noticeable-difference (JND) based VQA methodology has
been proposed as an alternative. It is observed experimentally that the JND
location is a normally distributed random variable. In this work, we explain
this distribution by proposing a user model that takes both subject
variabilities and content variabilities into account. This model is built upon
user's capability to discern the quality difference between video clips encoded
with different QPs. Moreover, it analyzes video content characteristics to
account for inter-content variability. The proposed user model is validated on
the data collected in the VideoSet. It is demonstrated that the model is
flexible to predict SUR distribution of a specific user group.
