Abstract Deep-learning based image quality assessment (IQA) algorithms usually use the transfer learning method that transfers a pre-trained network for classification task to handle IQA task. Although it can overcome the problem of having insufficient IQA databases to some extent, it cannot distinguish between the important and unimportant deep features for the IQA task, which potentially leads to inaccurate prediction performance. In this paper, we propose a no-reference IQA method based on modelling of deep feature importance. A SE-VGG network is developed by using adaptive transfer learning method. It can suppress the features of local parts of salient objects of images that are not important to the IQA task, and emphasize the features of image distortion and salient objects that are important to IQA task. Moreover, the structure of the SE-VGG is investigated to improve the accuracy of the image quality assessment on a small IQA database. Experiments are conducted to evaluate the performance of the proposed method on various databases, including the LIVE, TID2013, CSIQ, LIVE multiply distorted and LIVE challenge. The results show the proposed method significantly outperforms the state-of-the-art methods. In addition, our method demonstrates a strong generalization ability.

中文翻译：

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基于深度特征重要性意识的无参考图像质量预测

摘要 基于深度学习的图像质量评估 (IQA) 算法通常使用转移学习方法，该方法将用于分类任务的预训练网络转移到处理 IQA 任务。虽然它可以在一定程度上克服IQA数据库不足的问题，但它无法区分IQA任务的重要和不重要的深层特征，这可能导致预测性能不准确。在本文中，我们提出了一种基于深度特征重要性建模的无参考 IQA 方法。SE-VGG 网络是通过使用自适应迁移学习方法开发的。它可以抑制对IQA任务不重要的图像显着对象的局部特征，并强调对IQA任务重要的图像失真和显着对象的特征。而且，研究了 SE-VGG 的结构，以提高小型 IQA 数据库上图像质量评估的准确性。进行了实验以评估所提出方法在各种数据库上的性能，包括 LIVE、TID2013、CSIQ、LIVE 多失真和 LIVE 挑战。结果表明，所提出的方法明显优于最先进的方法。此外，我们的方法展示了强大的泛化能力。