SR-Ground: Image Quality Grounding for Super-Resolved Content
Super-Resolution (SR) has advanced rapidly in recent years, with diffusion-based models achieving unprecedented fidelity at the cost of introducing new types of visual artifacts. While existing Image Quality Assessment (IQA) methods provide holistic quality scores, they lack interpretability and fail to distinguish between different artifact types arising from modern SR approaches. To address this gap, we introduce SR-Ground, a large-scale dataset specifically designed for fine-grained artifact segmentation in super-resolved images. The dataset comprises images processed by a diverse set of state-of-the-art SR models, with pixel-level annotations for multiple artifact categories. We condu...
| Authors | Artem Borisov, Evgeney Bogatyrev, Khaled Abud et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 May 2026 |
| Last updated at source | 30 September 2026 |
Models and datasets referencing this paper
- Divotion/SR-GroundDataset
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