  In a subjective experiment to evaluate the perceptual audiovisual quality of
multimedia and television services, raw opinion scores collected from test
subjects are often noisy and unreliable. To produce the final mean opinion
scores (MOS), recommendations such as ITU-R BT.500, ITU-T P.910 and ITU-T P.913
standardize post-test screening procedures to clean up the raw opinion scores,
using techniques such as subject outlier rejection and bias removal. In this
paper, we analyze the prior standardized techniques to demonstrate their
weaknesses. As an alternative, we propose a simple model to account for two of
the most dominant behaviors of subject inaccuracy: bias and inconsistency. We
further show that this model can also effectively deal with inattentive
subjects that give random scores. We propose to use maximum likelihood
estimation to jointly solve the model parameters, and present two numeric
solvers: the first based on the Newton-Raphson method, and the second based on
an alternating projection (AP). We show that the AP solver generalizes the
ITU-T P.913 post-test screening procedure by weighing a subject's contribution
to the true quality score by her consistency (thus, the quality scores
estimated can be interpreted as bias-subtracted consistency-weighted MOS). We
compare the proposed methods with the standardized techniques using real
datasets and synthetic simulations, and demonstrate that the proposed methods
are the most valuable when the test conditions are challenging (for example,
crowdsourcing and cross-lab studies), offering advantages such as better
model-data fit, tighter confidence intervals, better robustness against subject
outliers, the absence of hard coded parameters and thresholds, and auxiliary
information on test subjects. The code for this work is open-sourced at
https://github.com/Netflix/sureal.
