Konstanz Image Quality 10k Database
KonIQ-10k is a large-scale IQA dataset consisting of 10,073 quality scored images. This is the first in-the-wild database aiming for ecological validity, with regard to the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models.
Variants: KonIQ-10k
This dataset is used in 2 benchmarks:
| Task | Model | Paper | Date |
|---|---|---|---|
| Image Quality Assessment | RealQA | Next Token Is Enough: Realistic … | 2025-03-08 |
| Image Quality Assessment | OneAlign | Q-Align: Teaching LMMs for Visual … | 2023-12-28 |
| Image Quality Assessment | UNIQA | You Only Train Once: A … | 2023-10-14 |
| Image Quality Assessment | KonCept512 | KonIQ-10k: Towards an ecologically valid … | 2018-03-22 |
Recent papers with results on this dataset: