M. Jenadeleh, R. Hamzaoui, U.-D. Reips, and D. Saupe, “Crowdsourced Estimation of Collective Just Noticeable Difference for Compressed Video with the Flicker Test and QUEST+,”
IEEE Transactions on Circuits and Systems for Video Technology, p. 1, May 2024, doi:
10.1109/tcsvt.2024.3402363.
BibTeX
M. Jenadeleh et al., “An Image Quality Dataset with Triplet Comparisons for Multi-dimensional Scaling.” IEEE, pp. 278–281, 2024. doi:
10.1109/qomex61742.2024.10598258.
BibTeX
D. Saupe and S. Hviid del Pin, “National differences in image quality assessment: An investigation on three large-scale IQA datasets,” in
2024 16th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, Ed., IEEE, May 2024, pp. 214–220. doi:
10.1109/qomex61742.2024.10598250.
BibTeX
M. Jenadeleh, A. Heß, S. Hviid del Pin, E. Gamboa, M. Hirth, and D. Saupe, “Impact of feedback on crowdsourced visual quality assessment with paired comparisons,” in
2024 16th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, Ed., IEEE, May 2024, pp. 125–131. doi:
10.1109/qomex61742.2024.10598256.
BibTeX
S. Su et al., “Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model,”
IEEE Transactions on Multimedia, vol. 26, pp. 2671–2685, 2024, doi:
10.1109/tmm.2023.3301276.
BibTeX
M. Jenadeleh, J. Zagermann, H. Reiterer, U.-D. Reips, R. Hamzaoui, and D. Saupe, “Relaxed forced choice improves performance of visual quality assessment methods,” in
2023 15th International Conference on Quality of Multimedia Experience (QoMEX), 2023, pp. 37–42. [Online]. Available:
https://ieeexplore.ieee.org/abstract/document/10178467BibTeX
O. Wiedemann, V. Hosu, S. Su, and D. Saupe, “Konx: cross-resolution image quality assessment,”
Quality and User Experience, vol. 8, Art. no. 1, Aug. 2023, doi:
10.1007/s41233-023-00061-8.
BibTeX
G. Chen, H. Lin, O. Wiedemann, and D. Saupe, “Localization of Just Noticeable Difference for Image Compression,” in
2023 15th International Conference on Quality of Multimedia Experience (QoMEX), Jun. 2023, pp. 61–66. doi:
10.1109/QoMEX58391.2023.10178653.BibTeX
X. Zhao et al., “CUDAS: Distortion-Aware Saliency Benchmark,”
IEEE Access, vol. 11, pp. 58025–58036, Jun. 2023, doi:
10.1109/access.2023.3283344.
BibTeX
M. Testolina, V. Hosu, M. Jenadeleh, D. Lazzarotto, D. Saupe, and T. Ebrahimi, “JPEG AIC-3 Dataset: Towards Defining the High Quality to Nearly Visually Lossless Quality Range,” in
15th International Conference on Quality of Multimedia Experience (QoMEX), 2023, pp. 55–60. [Online]. Available:
https://ieeexplore.ieee.org/document/10178554BibTeX
M. Zameshina et al., “Fairness in generative modeling: do it unsupervised!,” in
Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, Jul. 2022, pp. 320–323. doi:
10.1145/3520304.3528992.
BibTeX
J. Lou, H. Lin, D. Marshall, D. Saupe, and H. Liu, “TranSalNet: Towards perceptually relevant visual saliency prediction,”
Neurocomputing, vol. 494, pp. 455–467, 2022, [Online]. Available:
https://www.sciencedirect.com/science/article/pii/S0925231222004714BibTeX
H. Lin et al., “Large-Scale Crowdsourced Subjective Assessment of Picturewise Just Noticeable Difference,”
IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, Art. no. 9, 2022, [Online]. Available:
https://ieeexplore.ieee.org/document/9745537BibTeX
BibTeX
H. Lin, H. Men, Y. Yan, J. Ren, and D. Saupe, “Crowdsourced Quality Assessment of Enhanced Underwater Images - a Pilot Study,” in
Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, Sep. 2022, pp. 1–4. [Online]. Available:
https://ieeexplore.ieee.org/document/9900904BibTeX
BibTeX
BibTeX
F. Götz-Hahn, V. Hosu, H. Lin, and D. Saupe, “KonVid-150k : A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild,”
IEEE Access, vol. 9, pp. 72139–72160, 2021, doi:
10.1109/ACCESS.2021.3077642.
BibTeX
H. Lin, G. Chen, and F. W. Siebert, “Positional Encoding: Improving Class-Imbalanced Motorcycle Helmet use Classification,” in
2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 1194–1198. [Online]. Available:
https://ieeexplore.ieee.org/document/9506178BibTeX
BibTeX
BibTeX
B. Roziere et al., “Evolutionary Super-Resolution,” in
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, in GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 151–152. doi:
10.1145/3377929.3389959.
BibTeX
M. Lan Ha, V. Hosu, and V. Blanz, “Color Composition Similarity and Its Application in Fine-grained Similarity,” in
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Piscataway, NJ: IEEE, 2020, pp. 2548–2557. [Online]. Available:
https://ieeexplore.ieee.org/document/9093522BibTeX
V. Hosu, H. Lin, T. Szirányi, and D. Saupe, “KonIQ-10k : An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment,”
IEEE Transactions on Image Processing, vol. 29, pp. 4041–4056, 2020, [Online]. Available:
https://ieeexplore.ieee.org/document/8968750BibTeX
BibTeX
O. Wiedemann and D. Saupe, “Gaze Data for Quality Assessment of Foveated Video,” in
ACM Symposium on Eye Tracking Research and Applications, in ETRA ’20 Adjunct. New York, NY, USA: Association for Computing Machinery, 2020. doi:
10.1145/3379157.3391656.
BibTeX
T. Guha et al., “ATQAM/MAST′20: Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends,” in
Proceedings of the 28th ACM International Conference on Multimedia, in MM ’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 4758–4760. doi:
10.1145/3394171.3421895.
BibTeX
O. Wiedemann, V. Hosu, H. Lin, and D. Saupe, “Foveated Video Coding for Real-Time Streaming Applications,” in
2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 2020, pp. 1–6. [Online]. Available:
https://ieeexplore.ieee.org/abstract/document/9123080BibTeX
M. Jenadeleh, M. Pedersen, and D. Saupe, “Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition,”
Sensors, vol. 20, Art. no. 5, 2020, [Online]. Available:
https://www.mdpi.com/1424-8220/20/5/1308BibTeX
H. Men, V. Hosu, H. Lin, A. Bruhn, and D. Saupe, “Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database,” in
Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), 2020, pp. 1–6. [Online]. Available:
https://ieeexplore.ieee.org/document/9123096/authors#authorsBibTeX
V. Hosu et al., “From Technical to Aesthetics Quality Assessment and Beyond: Challenges and Potential,” in
Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends, in ATQAM/MAST′20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 19–20. doi:
10.1145/3423268.3423589.
BibTeX
X. Zhao, H. Lin, P. Guo, D. Saupe, and H. Liu, “Deep Learning VS. Traditional Algorithms for Saliency Prediction of Distorted Images,” in
2020 IEEE International Conference on Image Processing (ICIP), 2020, pp. 156–160. [Online]. Available:
https://ieeexplore.ieee.org/abstract/document/9191203BibTeX
H. Lin et al., “SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning,”
Quality and User Experience, vol. 5, Art. no. 1, 2020, doi:
10.1007/s41233-020-00034-1.
BibTeX
H. Lin, M. Jenadeleh, G. Chen, U.-D. Reips, R. Hamzaoui, and D. Saupe, “Subjective Assessment of Global Picture-Wise Just Noticeable Difference,” in
Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), 2020, pp. 1–6. [Online]. Available:
https://ieeexplore.ieee.org/document/9106058BibTeX
H. Men, V. Hosu, H. Lin, A. Bruhn, and D. Saupe, “Subjective annotation for a frame interpolation benchmark using artefact amplification,”
Quality and User Experience, vol. 5, Art. no. 1, 2020, [Online]. Available:
https://link.springer.com/article/10.1007%2Fs41233-020-00037-yBibTeX
H. Men, H. Lin, V. Hosu, D. Maurer, A. Bruhn, and D. Saupe, “Visual Quality Assessment for Motion Compensated Frame Interpolation,” in
Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019, pp. 1–6. [Online]. Available:
https://ieeexplore.ieee.org/document/8743221BibTeX
H. Lin, V. Hosu, and D. Saupe, “KADID-10k: A Large-scale Artificially Distorted IQA Database,” in
Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019, pp. 1–3. [Online]. Available:
https://ieeexplore.ieee.org/document/8743252BibTeX
C. Fan et al., “SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning,” in
Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019, pp. 1–6. [Online]. Available:
https://ieeexplore.ieee.org/document/8743204BibTeX
V. Hosu, B. Goldlücke, and D. Saupe, “Effective Aesthetics Prediction with Multi-level Spatially Pooled Features,”
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9367–9375, 2019, [Online]. Available:
https://ieeexplore.ieee.org/document/8953497BibTeX
H. Men, H. Lin, and D. Saupe, “Spatiotemporal Feature Combination Model for No-Reference Video Quality Assessment,” in
Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2018, pp. 1–3. [Online]. Available:
https://ieeexplore.ieee.org/document/8463426BibTeX
V. Hosu, H. Lin, and D. Saupe, “Expertise Screening in Crowdsourcing Image Quality,” in
Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2018, pp. 276–281. [Online]. Available:
https://ieeexplore.ieee.org/document/8463427BibTeX
M. Jenadeleh, M. Pedersen, and D. Saupe, “Realtime Quality Assessment of Iris Biometrics Under Visible Light,” in
Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPRW), CVPR Workshops, IEEE, 2018, pp. 443–452. [Online]. Available:
https://ieeexplore.ieee.org/document/8575548BibTeX
D. Varga, D. Saupe, and T. Szirányi, “DeepRN: A Content Preserving Deep Architecture for Blind Image Quality Assessment,” in
Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), IEEE, 2018, pp. 1–6. [Online]. Available:
https://ieeexplore.ieee.org/document/8486528BibTeX
M. Spicker, F. Hahn, T. Lindemeier, D. Saupe, and O. Deussen, “Quantifying Visual Abstraction Quality for Stipple Drawings,” in
Proceedings of the Symposium on Non-Photorealistic Animation and Rendering (NPAR), ACM, Ed., Association for Computing Machinery, 2017, pp. 8:1–8:10. doi:
10.1145/3092919.3092923.
BibTeX
V. Hosu et al., “The Konstanz natural video database (KoNViD-1k).,” in
Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2017, pp. 1–6. [Online]. Available:
https://ieeexplore.ieee.org/document/7965673BibTeX
S. Egger-Lampl et al., “Crowdsourcing Quality of Experience Experiments,” D. Archambault, H. Purchase, and T. Hossfeld, Eds., in Information Systems and Applications, incl. Internet/Web, and HCI. , Springer International Publishing, 2017, pp. 154–190.
BibTeX
U. Gadiraju et al., “Crowdsourcing Versus the Laboratory: Towards Human-centered Experiments Using the Crowd,” D. Archambault, H. Purchase, and T. Hossfeld, Eds., in Information Systems and Applications, incl. Internet/Web, and HCI. , Springer International Publishing, 2017, pp. 6–26.
BibTeX
I. Zingman, D. Saupe, O. A. B. Penatti, and K. Lambers, “Detection of Fragmented Rectangular Enclosures in Very High Resolution Remote Sensing Images,”
IEEE Transactions on Geoscience and Remote Sensing, vol. 54, Art. no. 8, 2016, [Online]. Available:
https://ieeexplore.ieee.org/document/7452408BibTeX
BibTeX
BibTeX
D. Saupe, F. Hahn, V. Hosu, I. Zingman, M. Rana, and S. Li, “Crowd Workers Proven Useful: A Comparative Study of Subjective Video Quality Assessment,” in
Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), 2016, pp. 1–2. [Online]. Available:
https://www.uni-konstanz.de/mmsp/pubsys/publishedFiles/SaHaHo16.pdfBibTeX