The Transregional Collaborative Research Centre 161 “Quantitative Methods for Visual Computing” is an interdisciplinary research centre at the University of Stuttgart and the University of Konstanz, funded by Deutsche Forschungsgemeinschaft (DFG) under project number 251654672. Ulm University and Ludwig-Maximilians-Universität München are participating institutions in the second and third funding period. The Max Planck Institute for Biological Cybernetics in Tübingen in was a participating institution in the first funding period.

The goal of SFB/Transregio 161 is establishing the paradigm of quantitative science in the field of visual computing, which is a long-term endeavour requiring a fundamental research effort broadly covering four research areas, namely quantitative models and measures, adaptive algorithms, interaction and applications. In the third funding period, which started in 2023, new research directions are being approached. One is visual explainability, assessing and quantifying how well the users of a visualisation system understand the phenomena shown visually. The second direction targets mixed reality, covering all forms of augmented and virtual reality as a cross-cutting field of various visual computing subfields, irrespective of applied technology. The third research theme aims to bring research results in the world, moving away from experiments in the laboratory and in the wild to openly accessible applications that provide research results, methods, data sets, and other outcomes from SFB/Transregio 161 to a wide range of stakeholders in academia, industry, teaching, and society in general.

In SFB/Transregio 161, approximately 40 scientists in the fields of computer science, visualisation, computer vision, human computer interaction, linguistics and applied psychology are jointly working on improving the quality of future visual computing methods and applications.

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Adobe PDF - 65.9 KB - MD5: ac83d5cf8016f66ad99f7c11ddca3ece
The full acknowledgements, which are shortened in the paper.
Tabular Data - 11.1 MB - 80 Variables, 9618 Observations - UNF:6:tFuPaFP2M33u7cGl7+pkjA==
Data
metrics_cropped.csv: CSV file containing objective IQA scores computed on the (840 \times 944) evaluation stimuli. For category-1 and category-2 sources, these correspond to the full reconstructed images; for category-3 sources, they correspond to the selected (840 \times 944) crops used in the subjective experiments.
Tabular Data - 11.0 MB - 80 Variables, 9618 Observations - UNF:6:iVXIHYYc6Nk0VStrjbnFHQ==
Data
metrics_fullres.tab: Tab-separated file containing objective IQA scores computed on the full-resolution reconstructed images
Markdown Text - 6.1 KB - MD5: 340f24a902c9bd4c3a134d3f3d93680f
Documentation
README.md: Markdown file providing an overview of the AIC2026 dataset, including dataset structure, file naming conventions, codec acronyms, accompanying metadata files, citation information, and licensing terms.
ZIP Archive - 2.2 GB - MD5: ae0baa080dc4e03896bda703f7218e5e
Data
Compressed bitstreams for all base codecs (AVIF, J2K, JAI, JPG, JXL) and all distortion levels.
ZIP Archive - 13.6 GB - MD5: f84daf630afeb40e7ab93542fd917585
Data
Full dataset (distorted images + reference sources) losslessly re-encoded as JPEG XL.
ZIP Archive - 21.4 GB - MD5: 6ced5048acc3776d5c483ed28381d523
Data
AIC2026_complete_dataset.zip: ZIP archive containing the AIC2026 image data, including the full resolution 70 pristine source images and 9,618 distorted versions generated by the 17 compression codecs / codec configurations.
ZIP Archive - 5.5 GB - MD5: 0dc6328f5dbd8aa8d5b75896a561aedc
Data
Cropped distorted images (840x944) and 70 reference crops, one per source, losslessly re-encoded as JPEG XL. JPEG XL version of AIC2026-dataset-cropped.zip.
ZIP Archive - 8.7 GB - MD5: f9010c61602bf631a049d167d3f4cf18
Data
Cropped distorted images (840×944) and 70 reference crops, one per source. Superset of AIC2026-PTC-stimuli.zip.
Tabular Data - 22.0 KB - 22 Variables, 70 Observations - UNF:6:eqLJRr7Fd4d9hArfNmiCrQ==
Data
AIC2026_source_images_metadata_and_attribution.csv: CSV file containing metadata, source information, licensing, and attribution for the 70 source images. Each row corresponds to one source image and includes the dataset image identifier, original filename, source and original dimensions, image origin, author, source URL, license, capture date, ori...
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