Guo, Xinyu2021-12-232021-12-232021-12-232021-12-16http://hdl.handle.net/10012/17818Images/videos are playing a more and more important role in the 21st century. The perceived quality of visual content often degrades during the process of acquisition, storage, transmission, display and rendering. Since subjective evaluation of such a large amount of visual content is impossible, the development of objective evaluation methods becomes highly desirable. Traditionally, there are three well established Image Quality Assessment (IQA) paradigms. They are Full Reference (FR) IQA which needs full access to the pristine quality reference, Reduced Reference (RR) IQA which requires partial information from the pristine reference and, No Reference (NR) IQA which does not require any reference information. While the strict requirement prohibits FR IQA from wide usage in many applications, RR and NR IQA methods cannot produce comparable performance. In the thesis, we aim to address this problem by exploring the Degraded Reference (DR) paradigm which makes no requirement on pristine reference but on reference of degraded quality, and at the same time, outperforms RR/NR methods. We address this problem in three steps. Firstly, we develop an FR model built upon a Deep Neural Network (DNN) that can handle multiply distorted images. The model structure of this FR model is then utilized to design DNN-based DR IQA models. We further improve the DR DNN model by adjusting the network structure. Finally, we use a two-step framework, which utilizes an NR model and an FR model as base modules followed by a regressor to create a single DR prediction for a given image. We test our models on subject-related datasets in IQA field. The testing results show that our FR model has state-of-the-art performance when handling multiply distorted images, and meanwhile produces great performance when handling singly distorted images. Our DR model developed using the two-step framework gives better performance than RR/NR models when the reference is not pristine.enimage quality assessmentdegraded referencedeep neural networksmultiply distorted imagesMaster Thesis