Blind image quality assessment based on progressive multi-task learning

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Due to the lack of adequate training data and sufficient mining of prior knowledge related to perceived quality, most existing image quality assessment (IQA) methods show limited generalization performance. In this paper, we study the prior knowledge from the factors affecting perceived quality, and introduce a novel progressive multi-task learning based blind IQA method. Inspired by the definition of IQA: human comprehensive perception for degradation of image content, we firstly deconstruct IQA into three elements, i.e., image content, pattern of degradation, and intensity of degradation. Based on these elements, we design the corresponding auxiliary tasks for instructing the network to learn IQA. By statistical analysis on a great deal of data, we find that there is progressive relevance among the four tasks. Furthermore, we mathematically derive that introducing the progressive relevance into a multi-task learning network can effectively constrain the hypothesis space of the main task. Under the guidance of the derivation, we propose an end-to-end IQA framework based on progressive multi-task learning. Experimental results demonstrate the excellent generalization capability of the proposed method, which achieves state-of-the-art performance against these existing IQA methods.