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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1 to 10 of 3,711 Results
Aug 25, 2026 - SFB-TRR 161 D04 "Quantitative Aspects of Immersive Analytics for the Life Sciences"
Feyer, Stefan Paul, 2025, "Source Code and Video: 2D versus 2.5D Layer Arrangement for Animal Behaviour Multiplex Networks", https://doi.org/10.18419/DARUS-5503, DaRUS, V2
This Data contains the Unity Program, especially written for the experiment published in the paper: 2D versus 2.5D Layer Arrangement for Animal Behaviour Multiplex Networks. It contains a network from an open-access dataset, which can be viewed under the study conditions. Furthermore, a video displays in first person the view through a VR HMD on th...
Adobe PDF - 65.9 KB - MD5: ac83d5cf8016f66ad99f7c11ddca3ece
The full acknowledgements, which are shortened in the paper.
Jul 30, 2026 - Associated Research
Jenadeleh, Mohsen; Sneyers, Jon; Ascenso, João, 2026, "JPEG AIC2026: Fine-grained image compression dataset", https://doi.org/10.18419/DARUS-6156, DaRUS, V2, UNF:6:XijMAc83sLBGJd3690d0/g== [fileUNF]
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. The data in this dataset represent Assessment of Image Coding 2026 (AIC2026), a large-scale dataset for high-fid...
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.
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