Abstract Image Distortion Identification is important for image processing system enhancement, image distortion correction and image quality assessment. Although images may suffer various number of distortions while going through different systems, most of the previous researches of image distortion identification were focus on identifying single distortion in image. In this paper, we proposed a CNN-based multi-label learning model (called MLLNet) to identify distortions for different scenarios, including images having no distortion, single distortion and multiple distortions. Concretely, we transform the multi-label classification for image distortion identification to a number of multi-class classifications and use a deep multi-task CNN model to train all associated classifiers simultaneously. For unseen image, we use the trained CNN model to predict a number of classifications at same time and fuse them to final multi-label classification. The extensive experiments demonstrate that the propose algorithm can achieve good performance on several databases. Moreover, the network architecture of the CNN model can make flexible adjustment according to the different requirements.

中文翻译：

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用于图像失真识别的深度多标签学习

摘要 图像失真识别对于图像处理系统增强、图像失真校正和图像质量评估具有重要意义。尽管图像在经过不同的系统时可能会出现不同数量的畸变，但以往的图像畸变识别研究大多集中在识别图像中的单个畸变上。在本文中，我们提出了一种基于 CNN 的多标签学习模型（称为 MLLNet）来识别不同场景的失真，包括无失真、单失真和多重失真的图像。具体来说，我们将用于图像失真识别的多标签分类转换为多个多类分类，并使用深度多任务 CNN 模型同时训练所有相关分类器。对于看不见的图像，我们使用经过训练的 CNN 模型同时预测多个分类，并将它们融合到最终的多标签分类中。大量实验表明，所提出的算法可以在多个数据库上取得良好的性能。而且，CNN模型的网络架构可以根据不同的需求进行灵活的调整。