The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can be effectively determined. The underlying mechanism of the proposed approach is based upon the mild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion. We empirically show that the statistics deviation could be effectively leveraged in quality assessment, and the proposed method is superior when evaluated in different settings. Extensive experimental results demonstrate the Deep Feature Statistics based SCI Quality Assessment (DFSS-IQA) model delivers promising performance compared with existing NR-IQA models and shows a high generalization capability in the cross-dataset settings. The implementation of our method is publicly available at https://github.com/Baoliang93/DFSS-IQA .

中文翻译：

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用于广义屏幕内容图像质量评估的深度特征统计映射

自然图像的统计规律，称为自然场景统计，在无参考图像质量评估中发挥着重要作用。然而，人们普遍认为，通常由计算机生成的屏幕内容图像 (SCI) 不包含此类统计数据。这里我们首次尝试学习SCI的统计数据，以此来有效判断SCI的质量。所提出方法的基本机制基于一个温和的假设，即非物理获得的 SCI 仍然遵循某些可以通过学习方式理解的统计数据。我们的经验表明，统计偏差可以在质量评估中得到有效利用，并且在不同设置下进行评估时，所提出的方法更为优越。大量的实验结果表明，与现有的 NR-IQA 模型相比，基于深度特征统计的 SCI 质量评估 (DFSS-IQA) 模型具有良好的性能，并在跨数据集设置中显示出较高的泛化能力。我们的方法的实现可在以下位置公开获取：https://github.com/Baoliang93/DFSS-IQA 。