Colourlab's official dataset and software repository is hosted on Zenodo. As part of our commitment to open science and reproducibility, we provide access to datasets generated through our research activities. These datasets are freely available for download and reuse under open licenses, supporting collaboration and innovation across the scientific community.

🔗 Browse our datasets on Zenodo: https://zenodo.org/communities/colourlab

List of datasets

Cherepkova, O. (2025). Individual Contrast Preferences Database [Data set]. I Journal of Imaging (Bd. 10, Nummer 1). Zenodo. https://doi.org/10.5281/zenodo.17430959

Tabassum, S. (2024). NeRF-4Scenes: A Video Dataset for Subjective Assessment of NeRF [Data set]. Zenodo. https://doi.org/10.18710/LFHFJN

Raed, H. (2024). Colourlab Imaging Database: Optical Aberrations [Data set]. London Imaging Meeting 2024 (LIM 2024), London, United Kingdom. Zenodo. https://doi.org/10.5281/zenodo.17463496

Cherepkova, O. (2024). Contrast preference: Three-Alternative Choice vs slider-based experiment [Data set]. the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Zenodo. https://doi.org/10.5281/zenodo.17431253

Ahmed, T. U. (2023). Image Demosaicing: subjective analysis and evaluation of image quality metrics [Data set]. Electronic Imaging 2023. Zenodo. https://doi.org/10.5281/zenodo.17416380

Elezabi, O. (2023). CID:SFA-hytexila [Data set]. I Sensors (Bd. 23, Nummer 5443). Zenodo. https://doi.org/10.5281/zenodo.17425910

Kadyrova, A. (2022). Image enhancement dataset for evaluation of image quality metrics [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17422800

Farup, I. (2002). ICC3D - A color management application. The tenth Color Imaging Conference (CIC). Zenodo. https://doi.org/10.5281/zenodo.17422356

Vats, A. (2022). Wireless Capsule Endoscopy Synthetic Atlas [Data set]. I Scientific Reports (Bd. 13, Numre 10857 (2023)). Zenodo. https://doi.org/10.5281/zenodo.17415794

Del Pin, S. H. (2022). Subjective quality evaluation: what can be learnt from cognitive science? [Data set]. The 11th Colour and Visual Computing Symposium 2022 (CVCS 2022). Zenodo. https://doi.org/10.5281/zenodo.17416243

Cherepkova, O. (2022). Analyzing the Variability of Subjective Image Quality Ratings for Different Distortions [Data set]. 2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Salzburg, Austria. Zenodo. https://doi.org/10.5281/zenodo.17415619

Deborah, H. (2022). Hyperspectral Pigments [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5592485

Azimian, S. (2021). Subjective Enhanced Image Dataset (SEID) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17425455

Pedersen, M. (2021). The Colourlab Image Dataset for Geometric Distortions [Data set]. Color and Imaging Conference (CIC). Zenodo. https://doi.org/10.5281/zenodo.17415506

Kadyrova, A. (2020). The Colourlab Image Dataset for 2.5D Quality Assessment [Data set]. Twenty-eight Color and Imaging Conference (CIC). Zenodo. https://doi.org/10.5281/zenodo.17422683

Amirshahi, S. A. (2019). The Colourlab Contrast Enhanced Image Dataset (CCEID) [Data set]. the 8th European Workshop on Visual Information Processing (EUVIP). Zenodo. https://doi.org/10.5281/zenodo.17415198

Mohammed, A. (2018). Colon capsule endoscopy images [Data set]. I Journal of Imaging (Bd. 4, Nummer 6, s. 75). Zenodo. https://doi.org/10.5281/zenodo.17406343

Kitanovski, V. (2017). Detection thresholds in chrominance channels of natural images [Data set]. The 10th International Symposium on Image and Signal Processing and Analysis, Ljubljana, Slovenia. Zenodo. https://doi.org/10.5281/zenodo.17415021

Kitanovski, V. (2016). Data Hiding in CDBS Halftones using Orientation Modulation [Data set]. I Journal of Imaging Science and Technology (Bd. 60, Nummer 5, s. 050407-1–050407-9). Zenodo. https://doi.org/10.5281/zenodo.17406265

Zhao, P. (2015). Colourlab Image Database:Perceptual Projection Sharpness (CID:PPS) [Data set]. Colour and Visual Computing Symposium 2015 (CVCS), Gjøvik., Norway. Zenodo. https://doi.org/10.5281/zenodo.17406200

Le Moan, S. (2015). Spectral Image Database for Quality (SIDQ) [Data set]. Electronic Imaging: Image Quality and System Performance XII, San Francisco, CA, USA. Zenodo. https://doi.org/10.5281/zenodo.17404025

Garcia Capel, L. E. (2014). Colourlab Image Database: Imai's ColorCheckers (CID:ICC) [Data set]. The 22nd Color and Imaging Conference (CIC), Boston, MA, USA. Zenodo. https://doi.org/10.5281/zenodo.17379114

Ziko, I. M. (2014). Colourlab Image Database: Multi-Illuminant scene (CID:MI) [Data set]. I Lecture Notes in Computer Science (Bd. 8509, s. 531–538). Zenodo. https://doi.org/10.5281/zenodo.17376649

Liu, X. (2014). Colourlab Image Database: Image Quality (CID:IQ) [Data set]. I Image and Signal Processing. ICISP 2014 (Bd. 8509). Zenodo. https://doi.org/10.5281/zenodo.17376246

Source codes

QuickEval

QuickEval is a web-based tool for psychometric image evaluation. It supports rank order, paired comparison and category judgement. The tool is provided by the Norwegian Colour and Visual Computing Laboratory. QuickEval is available on www.quickeval.no   

Source code available at https://github.com/khaivngo/QuickEval  

Citation:  Ngo, K. V.; Dokkeberg, C. A.; Storvik, J. J.; Farup, I. & Pedersen, M. Quickeval: a web application for subjective image quality assessment, Image Quality and System Performance XII, 2015, 9396, 9396-24

From labels to priors in capsule endoscopy

Pretrained weights from our paper From labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels . 

Citation: Vats, A., Mohammed, A. and Pedersen, M., 2022. From labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels. Scientific Reports, 12(1), p.15708.

Download

CIE Functions

Python module, GUI app, and web app for computing the CIE TC1-97 colorimetric functions. Available at https://github.com/ifarup/ciefunctions