  In this paper, we propose a highly efficient method to estimate an image's
mean opinion score (MOS) from a single opinion score (SOS). Assuming that each
SOS is the observed sample of a normal distribution and the MOS is its unknown
expectation, the MOS inference is formulated as a maximum likelihood estimation
problem, where the perceptual correlation of pairwise images is considered in
modeling the likelihood of SOS. More specifically, by means of the
quality-aware representations learned from the self-supervised backbone, we
introduce a learnable relative quality measure to predict the MOS difference
between two images. Then, the current image's maximum likelihood estimation
towards MOS is represented by the sum of another reference image's estimated
MOS and their relative quality. Ideally, no matter which image is selected as
the reference, the MOS of the current image should remain unchanged, which is
termed perceptual cons tancy constrained calibration (PC3). Finally, we
alternatively optimize the relative quality measure's parameter and the current
image's estimated MOS via backpropagation and Newton's method respectively.
Experiments show that the proposed method is efficient in calibrating the
biased SOS and significantly improves IQA model learning when only SOSs are
available.
