JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding
Recent advances in conventional and learning-based image coding have increased the demand for benchmark datasets that support fine-grained assessment of compressed image quality, particularly for learning-based image compression methods. This paper introduces Assessment of Image Coding 2026 (AIC2026), a large-scale dataset for high-fidelity image compression containing 70 source images selected from 2,787 candidates using semantic clustering, inter-metric disagreement among objective image quality assessment (IQA) methods, and manual inspection and refinement. The dataset covers a wide range of compression artifacts produced by eight conventional and four learning-based codecs across 17 c...
| Authors | Mohsen Jenadeleh, Jon Sneyers, João Ascenso et al. |
|---|---|
| Primary category | eess.IV |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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