The datasets of most image quality assessment studies contain ratings on a categorical scale with five levels, from bad (1) to excellent (5). For each stimulus, the number of ratings from 1 to 5 is summarized and given in the form of the mean opinion score. In this study, we investigate families of multinomial probability distributions parameterized by mean and variance that are used to fit the empirical rating distributions. To this end, we consider quantized metric models based on continuous distributions that model perceived stimulus quality on a latent scale. The probabilities for the rating categories are determined by quantizing the corresponding random variables using threshold values. Furthermore, we introduce a novel discrete maximum entropy distribution for a given mean and variance. We compare the performance of these models and the state of the art given by the generalized score distribution for two large data sets, KonIQ-10k and VQEG HDTV. Given an input distribution of ratings, our fitted two-parameter models predict unseen ratings better than the empirical distribution. In contrast to empirical ACR distributions and their discrete models, our continuous models can provide fine-grained estimates of quantiles of quality of experience that are relevant to service providers to satisfy a target fraction of the user population.

翻译：大多数图像质量评估研究的数据集采用五级分类量表进行评分，范围从差（1）到优（5）。对于每个刺激物，从1到5的评分数量被汇总并以平均意见分的形式给出。在本研究中，我们探究了以均值和方差为参数的多项式概率分布族，用于拟合经验评分分布。为此，我们考虑基于连续分布的量化度量模型，这些模型在潜在尺度上建模感知刺激质量。评分类别的概率通过使用阈值对相应随机变量进行量化来确定。此外，我们针对给定的均值和方差引入了一种新颖的离散最大熵分布。我们在两个大型数据集（KonIQ-10k和VQEG HDTV）上比较了这些模型与由广义分数分布代表的最先进方法的性能。给定输入的评分分布，我们拟合的双参数模型比经验分布能更好地预测未见评分。与经验ACR分布及其离散模型相比，我们的连续模型能够提供细粒度的体验质量分位数估计，这对于服务提供商满足目标用户群体的比例至关重要。