JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding

Mohsen Jenadeleh; Jon Sneyers; João Ascenso; Thomas Richter; Alexander Karabutov; Panqi Jia; Elena Alshina; Osamu Watanabe; António Pinheiro; Touradj Ebrahimi; Dietmar Saupe

arXiv · 2607.22783

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding

Mohsen Jenadeleh·Jon Sneyers·João Ascenso·Thomas Richter·Alexander Karabutov·Panqi Jia·Elena Alshina·Osamu Watanabe·António Pinheiro·Touradj Ebrahimi·Dietmar Saupe

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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 0.2-4.0 just-noticeable difference (JND) units using the ColorVideoVDP (CVVDP) metric for distortion estimation, yielding 9,618 distorted images. This fine-grained sampling enables analysis of rate-distortion behavior and objective metric evaluation for subtle quality differences across a wide range of compression artifacts. We report an extensive objective analysis using 24 conventional and 12 learning-based IQA methods. The results show substantial disagreement among current IQA methods for fine-grained quality differences, particularly for artifacts introduced by learning-based codecs. The complete dataset is publicly available at https://doi.org/10.18419/DARUS-6156.

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Mohsen Jenadeleh, Jon Sneyers, João Ascenso, Thomas Richter, Alexander Karabutov, Panqi Jia, Elena Alshina, Osamu Watanabe, António Pinheiro, Touradj Ebrahimi, Dietmar Saupe. 2026-07-24. JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding. https://arxiv.org/abs/2607.22783

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