JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding 文章
详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- Mohsen Jenadeleh, Jon Sneyers, Jo\~ao Ascenso, Thomas Richter, Alexander Karabutov, Panqi Jia, Elena Alshina, Osamu Watanabe, Ant\'onio Pinheiro, Touradj Ebrahimi, Dietmar Saupe
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-07-28
摘要
arXiv:2607.22783v1 Announce Type: cross Abstract: 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 coding configurations. Each source image is encoded using seven codecs. For each source-codec pair, decoded images are provided at 20 perceptually spaced distortion levels, corresponding approximately to 0.2-4.