An image processing system may bring distortions to the images going through it due to its inherent attributes or defects. Identifying the distortion types in a system is important to solve the system defects and improve its service quality. In this paper, we propose a novel algorithm to analyse the systemic distortions with multiple deep distortion directed image quality assessment models. Specifically, We pre-train several image quality assessment models that target to different distortions respectively. We use multiple images as anchor to test the system. We use the pre-trained IQA models to evaluate the anchor images that have gone through the system, and explore the features about the relationship between distortion types and quality scores. To analyse multiple distortions in a system, we embed dictionary learning into the multi-label classification framework. The experimental results demonstrate that our algorithm has good effectiveness, robustness and extensibility.

由于其固有属性或缺陷，图像处理系统可能会给通过它的图像带来失真。识别系统中的失真类型对于解决系统缺陷和提高其服务质量具有重要意义。在本文中，我们提出了一种新的算法来分析具有多个深度失真定向图像质量评估模型的系统失真。具体来说，我们预训练了几个分别针对不同失真的图像质量评估模型。我们使用多个图像作为锚点来测试系统。我们使用预训练的 IQA 模型来评估已经通过系统的锚图像，并探索失真类型和质量分数之间关系的特征。为了分析系统中的多重失真，我们将字典学习嵌入到多标签分类框架中。实验结果表明，我们的算法具有良好的有效性、鲁棒性和可扩展性。