{"type":"ScholarlyArticle","title":"Deep Opinion-Unaware Blind Image Quality Assessment by Learning and Adapting from Multiple Annotators","authors":[{"name":"Zhihua Wang","slug":"zhihua-wang","given":"Zhihua","family":"Wang","affiliation":""},{"name":"Xuelin Liu","slug":"xuelin-liu","given":"Xuelin","family":"Liu","affiliation":""},{"name":"Jiebin Yan","slug":"jiebin-yan","given":"Jiebin","family":"Yan","affiliation":""},{"name":"Jie Wen","slug":"jie-wen","given":"Jie","family":"Wen","affiliation":""},{"name":"Wei Wang","slug":"wei-wang","given":"Wei","family":"Wang","affiliation":""},{"name":"Chao Huang","slug":"chao-huang","given":"Chao","family":"Huang","affiliation":""}],"abstract":"Existing deep neural network (DNN)-based blind image quality assessment (BIQA) methods primarily rely on human-rated datasets for training. However, collecting human labels is extremely time-consuming and labor-intensive, posing a significant bottleneck for practical applications. To address this challenge, we propose a Deep opinion-Unaware BIQA model by learning and adapting from Multiple Annotators, termed DUBMA,  thereby eliminating the need for human annotations. Specifically, we first generate a large-scale set of distorted image pairs and then assign relative quality rankings using existing full-reference IQA models. The resulting dataset is subsequently employed for training our DUBMA.\r\nDue to the inherent discrepancies between synthetic and real-world distortions, a domain shift may occur. To address this, we propose an outlier-robust unsupervised domain adaptation approach leveraging optimal transport. This strategy effectively reduces the gap between synthetic and real-world distortion domains, thereby boosting the model’s adaptability and overall performance. Extensive experiments show that DUBMA outperforms existing opinion-unaware BIQA methods in terms of prediction accuracy across multiple datasets.","doi":"10.24963/ijcai.2025/227","url":"https://ijcai.org/proceedings/2025/227","pdf":"https://ijcai.org/proceedings/2025/0227.pdf","markdown":"https://ijcai.org/proceedings/2025/227.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/227","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":227,"pages":{"first":2036,"last":2044},"keywords":["Computer Vision: CV: Computational photography","Computer Vision: CV: Low-level Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}