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.

翻译：本文提出了一种高效方法，可从单一意见评分中估计图像的均值意见评分（MOS）。假设每个单一意见评分（SOS）是正态分布的观测样本，而MOS是其未知期望，则MOS推断被建模为最大似然估计问题，其中在构建SOS似然函数时考虑了成对图像间的感知相关性。具体而言，借助从自监督骨干网络中学习到的质量感知表征，我们引入了一种可学习的相对质量度量来预测两幅图像间的MOS差异。随后，当前图像面向MOS的最大似然估计可表示为另一参考图像的估计MOS与二者相对质量之和。理想情况下，无论选择哪幅图像作为参考，当前图像的MOS应保持不变，这被称为感知恒常性约束校准（PC3）。最后，我们通过反向传播和牛顿法分别交替优化相对质量度量参数与当前图像的估计MOS。实验表明，所提方法能有效校准有偏的SOS，并在仅能获得SOS时显著提升IQA模型的学习效果。