Data · dataset · 2026
JPEG AIC2026: Fine-grained image compression dataset
Listed in DaRUS
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.
Description
The data in this dataset represent 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 8 conventional and 4 learning-based codecs across 17 coding configurations. Each source image is encoded using 7 codecs. For each source-codec pair, decoded images are provided at 20 perceptually spaced distortion levels, corresponding approximately to 0.2-4.0 JND using CVVDP for distortion estimation, yielding 9,618 distorted images.
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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. An extensive objective analysis using 24 conventional and 12 learning-based IQA methods shows substantial disagreement among current IQA methods for fine-grained quality differences, particularly for artifacts introduced by learning-based codecs.
More information about the dataset including dataset structure, file naming conventions, codec acronyms, accompanying metadata files, citation information, and licensing terms. can be found in the README.
Links
Where it is published
- Dataverse dataset page darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi%3A10.18419%2FDARUS-6156 ↗
landing page · from darus uni stuttgart de
- DOI doi.org/10.18419/darus-6156 ↗
DOI / persistent id · from darus uni stuttgart de
Catalogue records · 1
- Dataverse API darus.uni-stuttgart.de/api/datasets/:persistentId/?persistentId=doi%3A10.18419%2FDARU… ↗
metadata API · from darus uni stuttgart de
Topics
- Stated by source
- Computer and Information Science
- Inferred from text
- Image 75%
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DaRUS | doi:10.18419/DARUS-6156 | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].dataverse_subject:computer-and-information-science | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /subjects |
| concepts[modality].local:modality:image | enrichment · darus uni stuttgart de | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | |
| description | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /description |
| publication_date | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | |
| title | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | /name |
| updated_date | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 | |
| version_label | source · darus uni stuttgart de | connector:darus_uni_stuttgart_de@1.0.0 |