{"ID":23475257,"CreatedAt":"2026-09-18T01:09:05.407443952Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.20247","arxiv_id":"2609.20247","title":"Unifying Image Quality Assessment Datasets: MOSAIQ-500K and MOSAIQ-Bench","abstract":"Image quality assessment (IQA) datasets use different subjective protocols and rating scales, so their scores are not directly comparable. The lack of a common perceptual scale hinders multi-dataset training and precludes direct inter-dataset evaluation. We address this by conducting a new subjective experiment and using its ratings as perceptual anchors to fit monotonic mappings that place the existing scores of 23 IQA datasets on a common quality scale while preserving within-dataset rankings. The resulting dataset, MOSAIQ-500K, contains over 500,000 images and is, to our knowledge, the largest IQA dataset with perceptually aligned subjective scores. We also propose MOSAIQ-Bench, an inter-dataset benchmark, and use it to evaluate 31 IQA methods, revealing substantial gaps between intra- and inter-dataset performance, particularly on authentic distortions. The aligned scores offer a key advantage: replacing specialized multi-dataset training mechanisms with standard regression losses yields comparable intra-dataset accuracy and better inter-dataset performance. MOSAIQ thus provides a practical foundation for combining heterogeneous IQA datasets to train more generalizable IQA models. Code and dataset will be made public at ivc.uwaterloo.ca/projects/unifying_iqa_datasets/ upon acceptance.","short_abstract":"Image quality assessment (IQA) datasets use different subjective protocols and rating scales, so their scores are not directly comparable. The lack of a common perceptual scale hinders multi-dataset training and precludes direct inter-dataset evaluation. We address this by conducting a new subjective experiment and usi...","url_abs":"https://arxiv.org/abs/2609.20247","url_pdf":"https://arxiv.org/pdf/2609.20247v1","authors":"[\"Wenbo Yang\",\"Zhongling Wang\",\"Jialu Xu\",\"Jinghan Zhou\",\"Zhou Wang\"]","published":"2026-09-17T12:53:03Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[]","has_code":false}