Predicting Single Observer’s Votes from Objective Measures using Neural Networks

The last decades witnessed an increasing number of works aiming at proposing objective measures for media quality assessment, i.e. determining an estimation of the mean opinion score (MOS) of human observers. In this contribution, we investigate a possibility of modeling and predicting single observer’s opinion scores rather than the MOS. More precisely, we attempt to approximate the choice of one single observer by designing a neural network (NN) that is expected to mimic that observer behavior in terms of visual quality perception. Once such NNs (one for each observer) are trained they can be looked at as ”virtual observers” as they take as an input information about a sequence and they output the score that the related observer would have given after watching that sequence. This new approach allows to automatically get different opinions regarding the perceived visual quality of a sequence whose quality is under investigation and thus estimate not only the MOS but also a number of other statistical indexes such as, for instance, the standard deviation of the opinions. Large numerical experiments are performed to provide further insight into a suitability of the approach.