Teacher-Guided Learning for Blind Image Quality Assessment

Zewen Chen, Juan Wang, Bing Li, Chunfeng Yuan, Weihua Xiong, Rui Cheng, Weiming Hu; Proceedings of the Asian Conference on Computer Vision (ACCV), 2022, pp. 2457-2474


The performance of deep learning models for blind image quality assessment (BIQA) suffers from annotated data insufficiency. However, image restoration, as a closely-related task with BIQA, can easily acquire training data without annotation. Moreover, both image semantic and distortion information are vital knowledge for the two tasks to predict and improve image quality. Inspired by these, this paper proposes a novel BIQA framework, which builds an image restoration model as a teacher network (TN) to learn the two aspects of knowledge and then guides the student network (SN) for BIQA. In TN, multi-branch convolutions are leveraged for performing adaptive restoration from diversely distorted images to strengthen the knowledge learning. Then the knowledge is transferred to the SN and progressively aggregated by computing long-distance responses to improve BIQA on small annotated data. Experimental results show that our method outperforms many state-of-the-arts on both synthetic and authentic datasets. Besides, the generalization, robustness and effectiveness of our method are fully validated. The code is available in https://github.com/chencn2020/TeacherIQA.

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@InProceedings{Chen_2022_ACCV, author = {Chen, Zewen and Wang, Juan and Li, Bing and Yuan, Chunfeng and Xiong, Weihua and Cheng, Rui and Hu, Weiming}, title = {Teacher-Guided Learning for Blind Image Quality Assessment}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {December}, year = {2022}, pages = {2457-2474} }