Image quality assessment is an important field in computer vision, since it has a great impact on related tasks. To meet these needs, a plethora of metrics has been developed. In this paper, we propose an efficient method that estimates the quality of 2D images without access to the pristine image. This metric is modeled based on the relevant patches selected by saliency information and a convolution neural network. To exploit the saliency information, only the more perceptually relevant patches that impact subjective judgment more, are considered. To this end, we first compute the saliency map of the distorted image. Then, a scanpath predictor that aims to mimic the visual behavior is employed as patch selector. Finally, a CNN model is used to predict the quality score through the extracted patches. To the best of our knowledge this is the first study to associate a scanpath prediction method and CNN to assess the quality of 2D images. Four CNN models were compared (AlexNet, VGG16, VGG19 and ResNet50) and the performance of the best CNN was compared to the state-of-the-art on four datasets. Experimental results demonstrated the efficiency of the proposed approach and its generalization capacity.

图像质量评估是计算机视觉的重要领域，因为它对相关任务有很大影响。为了满足这些需求，已经开发了许多指标。在本文中，我们提出了一种无需访问原始图像即可估算2D图像质量的有效方法。该度量基于显着性信息和卷积神经网络选择的相关补丁进行建模。为了利用显着性信息，仅考虑在感知上更相关的补丁，这些补丁对主观判断的影响更大。为此，我们首先计算失真图像的显着图。然后，将旨在模仿视觉行为的扫描路径预测器用作面片选择器。最后，使用CNN模型通过提取的补丁预测质量得分。据我们所知，这是第一项将扫描路径预测方法与CNN相关联以评估2D图像质量的研究。比较了四个CNN模型（AlexNet，VGG16，VGG19和ResNet50），并在四个数据集上将最佳CNN的性能与最新技术进行了比较。实验结果证明了该方法的有效性及其推广能力。