The majority of existing objective Image Quality Assessment (IQA) methods are designed specifically for singly and globally distorted images, which are incapable of dealing with locally and multiply distorted images effectively. On the one hand, artificially extracted features in traditional IQA methods are insufficient to represent quality variations in locally and multiply distorted images. On the other hand, the IQA methods suitable for both locally and multiply distorted images are scarce. In view of this, an IQA method based on multi-stage deep Convolutional Neural Networks (CNNs) is proposed for locally and multiply distorted images in this paper. The method adopts a three-stage strategy, which are distortion classification, quality prediction of single distortion and comprehensive assessment, respectively. Firstly, three datasets of locally, multiply and singly distorted images are designed and established. Secondly, a local and multiple distortion classifier, a distortion type classifier and prediction models of single distortions are obtained based on CNN models in their corresponding stages. Thirdly, the predicted results of single distortions are weighted by the output confidence probability of the classifiers, thus obtaining the final comprehensive quality. Experimental results verified the advantages of the proposed method in measuring the quality of locally and multiply distorted images.

现有的大多数客观图像质量评估（IQA）方法都是专门针对单个和全局失真的图像而设计的，这些方法无法有效地处理局部和失真图像。一方面，传统IQA方法中人为提取的特征不足以表示局部的质量变化和多重失真的图像。另一方面，缺乏适用于局部图像和倍数失真图像的IQA方法。有鉴于此，本文提出了一种基于多级深度卷积神经网络（CNN）的IQA方法，用于局部和倍增失真图像。该方法采用三个阶段的策略，分别是失真分类，单个失真的质量预测和综合评估。首先，三个本地数据集 设计并建立了倍增和单倍失真的图像。其次，基于CNN模型在相应阶段获得了局部和多种失真分类器，失真类型分类器和单个失真的预测模型。第三，通过分类器的输出置信概率对单个失真的预测结果进行加权，从而获得最终的综合质量。实验结果证明了该方法在测量局部和多重畸变图像质量方面的优势。通过分类器的输出置信概率对单个失真的预测结果进行加权，从而获得最终的综合质量。实验结果证明了该方法在测量局部和多重畸变图像质量方面的优势。通过分类器的输出置信概率对单个失真的预测结果进行加权，从而获得最终的综合质量。实验结果证明了该方法在测量局部和多重畸变图像质量方面的优势。