Generalized Score Distribution: A Two-Parameter Discrete Distribution Accurately Describing Responses From Quality of Experience Subjective Experiments

Subjective responses from Multimedia Quality Assessment (MQA) experiments are conventionally analyzed with methods not suitable for the data type these responses represent. Furthermore, obtaining subjective responses is resource intensive. Thus, a method that allows the reuse of existing responses would be beneficial. Applying improper data analysis methods leads to difficulty in interpreting results. This increases the probability of drawing erroneous conclusions. Building upon existing subjective responses is resource friendly and helps develop machine learning (ML) based visual quality predictors. In this work, we show that using a discrete model for analyzing responses from MQA subjective experiments is feasible. We indicate that our proposed Generalized Score Distribution (GSD) properly describes response distributions observed in typical MQA experiments. We also highlight interpretability of GSD parameters and indicate that the GSD outperforms the approach based on sample empirical distribution when it comes to bootstrapping. Furthermore, we provide evidence that the GSD outcompetes the state-of-the-art model both in terms of goodness-of-fit and bootstrapping capabilities. To accomplish the aforementioned objectives, we analyze more than one million subjective responses from over 30 subjective experiments.