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.

本文提出了一种高效的方法，从单个意见分数（SOS）估计图像的平均意见分数（MOS），通过最大似然估计来表征 MOS。我们假设每个 SOS 是正态分布的观测样本，MOS 是其未知期望值，并考虑了成对图像的感知相关性以对 SOS 的似然建模。通过使用自监督骨干学习的质量感知表示，引入了可学习的相对质量度量来预测两个图像之间的 MOS 差异。然后，当前图像的最大似然估计 MOS 由另一个参考图像的估计 MOS 和它们的相对质量之和表示。通过反向传播和牛顿法交替优化相对质量度量参数和当前图像的估计 MOS。实验证明，当只有 SOS 可用时，该方法在校准有偏 SOS 方面效率高，并显著改善了 IQA 模型的学习。