Maximum entropy and quantized metric models for absolute category ratings

TL;DR

This paper introduces novel maximum entropy and quantized metric models for better fitting and predicting categorical image quality ratings, enabling more precise quality estimates for service optimization.

Contribution

It proposes a new discrete maximum entropy distribution and compares it with existing models, improving rating prediction accuracy on large datasets.

Findings

Models outperform empirical distributions in predicting unseen ratings.

Continuous models provide detailed quantile estimates of perceived quality.

The approach enhances quality assessment for service providers.

Abstract

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.