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.
- Pub Date:
- April 2020
- Computer Science - Multimedia;
- Electrical Engineering and Systems Science - Image and Video Processing
- 14 pages, updated version of the original paper published in Human Vision and Electronic Imaging (HVEI) 2020