广义分数分布：精确描述经验质量主观实验响应的双参数离散分布
Generalised Score Distribution: A Two-Parameter Discrete Distribution Accurately Describing Responses from Quality of Experience Subjective Experiments
摘要：
多媒体质量评估（MQA）实验的主观响应通常用不适合这些响应所代表的数据类型的方法进行分析。此外，获得主观响应是资源密集型的。因此，允许重新使用现有响应的方法将是有益的。使用不正确的数据分析方法导致难以解释结果。这会导致错误的结论。建立在现有的主观反应是资源友好的，并有助于开发基于机器学习（ML）的视觉质量预测。我们表明，使用一个离散的模型来分析MQA主观实验的反应是可行的。我们表明，我们提出的广义分数分布（GSD）适当地描述了在典型的MQA实验中观察到的响应分布。我们强调GSD参数的可解释性，并指出GSD优于基于样本经验分布的方法，当涉及到自举。我们的证据表明，GSD的国家的最先进的模型，无论是在良好的拟合和自举能力。为了做到这一点，我们分析了来自30多个主观实验的100多万个主观反应。此外，我们通过GitHub存储库提供实现GSD模型和相关分析的代码：https://github.com/Qub3k/subjective-exp-consistency-check
英文摘要：
Subjective responses from Multimedia Quality Assessment (MQA) experiments are conventionally analysed with methods not suitable for the data type these responses represent. Furthermore, obtaining subjective responses is resource intensive. A method allowing reuse of existing responses would be thus beneficial. Applying improper data analysis methods leads to difficult to interpret results. This encourages drawing erroneous conclusions. Building upon existing subjective responses is resource friendly and helps develop machine learning (ML) based visual quality predictors. We show that using a discrete model for analysis of responses from MQA subjective experiments is feasible. We indicate that our proposed Generalised Score Distribution (GSD) properly describes response distributions observed in typical MQA experiments. We highlight interpretability of GSD parameters and indicate that the GSD outperforms the approach based on sample empirical distribution when it comes to bootstrapping. We evidence that the GSD outcompetes the state-of-the-art model both in terms of goodness-of-fit and bootstrapping capabilities. To do all of that we analyse more than one million subjective responses from more than 30 subjective experiments. Furthermore, we make the code implementing the GSD model and related analyses available through our GitHub repository: https://github.com/Qub3k/subjective-exp-consistency-check
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
