Dissemination List

  1. Stepanov, M., Valenzise, G. and Dufaux, F., 2020, October. Hybrid Learning-based and HEVC-based Coding of Light Fields. In IEEE International Conference on Image Processing (ICIP’2020).
    Open Access
  2. Agrawal, S., Simon, A., Bech, S., Bæntsen, K. and Forchhammer, S., 2020. Defining Immersion: Literature Review and Implications for Research on Audiovisual Experiences. Journal of the Audio Engineering Society, 68(6), pp.404-417.
    DOI: 10.17743/jaes.2020.0039
    Open Access
  3. Ak, A., Ling, S. and Le Callet, P., 2020, July. No-Reference Quality Evaluation of Light Field Content Based on Structural Representation of The Epipolar Plane Image. In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 1-6). IEEE.
    DOI: 10.1109/ICMEW46912.2020.9105975
    Open Access
  4. Mukati, M.U., Stepanov, M., Valenzise, G., Dufaux, F. and Forchhammer, S., 2020, July. View Synthesis-based Distributed Light Field Compression. In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 1-6). IEEE.
    DOI: 10.1109/ICMEW46912.2020.9105980
    Open Access
  5. Hexley, A.C., Yöntem, A.Ö., Spitschan, M., Smithson, H.E. and Mantiuk, R., 2020. Demonstrating a multi-primary high dynamic range display system for vision experiments. JOSA A, 37(4), pp.A271-A284.
    DOI: 10.1364/JOSAA.384022
    Open Access
  6. Gul, M. S. K., Bätz M., Ziegler M., Keinert J., and Fößel S. Multicamera Lightfield: High Dynamic Range Dataset Specification
  7. Yue, D., Gul, M.S.K., Bätz, M., Keinert, J. and Mantiuk, R., 2020, July. A Benchmark of Light Field View Interpolation Methods. In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 1-6). IEEE.
    DOI: 10.1109/ICMEW46912.2020.9106041
    Open Access
  8. Gul, M.S.K., Wolf, T., Bätz, M., Ziegler, M. and Keinert, J., 2020, July. A High-Resolution High Dynamic Range Light-Field Dataset with an Application to View Synthesis and Tone-Mapping. In 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (pp. 1-6). IEEE.
    DOI: 10.1109/ICMEW46912.2020.9105964
  9. Mukati, M.U. and Forchhammer, S., 2020, March. EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images. In 2020 Data Compression Conference (DCC) (pp. 43-52). IEEE.
    DOI: 10.1109/DCC47342.2020.00012
    Open Access
  10. Fela, R., Zacharov, N. and Forchhammer, S. 2020. Towards a Perceived Audiovisual Quality Model for Immersive Content. QoMEX 2020.
    Open Access
  11. Wolski, K., Giunchi, D. and Kinuwaki, S. et al. 2019. Selecting Texture Resolution Using a Task‐specific Visibility Metric. Computer Graphics Forum 38, 7, 685-696.
    DOI: 10.1111/cgf.13871
    Project Page
  12. Ak, A., and Callet, P. 2019. Towards Perceptually Plausible Training of Image Restoration Neural Networks. International Conference on Image Processing Theory, Tools and Applications.
    DOI: 10.1109/IPTA.2019.8936096
    Open Access
  13. Ak, A., and Callet, P. 2019. Investigating Epipolar Plane Image Representations for Objective Quality Evaluation of Light Field Images. European Workshop on Visual Information Processing.
    Best Student Paper
    DOI: 10.1109/EUVIP47703.2019.8946194
    Open Access
  14. Gul, M. S. K., Bätz, M., and Keinert, J. 2019. Pixel-Wise Confidences for Stereo Disparities Using Recurrent Neural Networks. British Machine Vision Conference.
    Open Access
  15. Agrawal, S., Simon, A., Bech, S., Bærentsen, K., and Forchhammer, S. 2019. Defining Immersion: Literature Review and Implications for Research on Immersive Audiovisual Experiences. Audio Engineering Society Convention 147, 10275.
    Open Access
  16. Zhong, F., Koulieris, G. and Drettakis, G. et al. 2019. DiCE: Dichoptic Contrast Enhancement for VR and Stereo Displays. ACM Transactions on Graphics 38, 6, Article 211.
    DOI: 10.1145/3355089.3356552
    Open Access
    Project Page
  17. Ye, N., Wolski, K. and Mantiuk, R. 2019. Predicting Visible Image Differences Under Varying Display Brightness and Viewing Distance. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5434-5442.
    DOI: 10.1109/CVPR.2019.00558
    Open Access
    Project Page
  18. Wolski, K., Giunchi, D. and Ye, N. et al. 2018. Dataset and Metrics for Predicting Local Visible Differences. ACM Transactions on Graphics 37, 5, 1-14.
    DOI: 10.1145/3196493
    Open Access
    Project Page
 
Updated by Jingyu Liu on 8 December 2020
Updated by Jingyu Liu on 8 December 2020

ITN RealVision

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 765911.

Key Roles
ITN RealVision Coordinator: Søren Forchhammer, DTU
Training Coordinator: Patrick Le Callet, UN
Scientific Coordinator: Søren Forchhammer, DTU
Industrial Coordinator: DxO
Recruitment and Equal Opportunities Coordinator: DTU
Communication Coordinator: Rafal Mantiuk, UCAM
Project Administrator: Mie Reippuert, DTU

Information
Project Number:765911
Call: H2020-MSCA-ITN-2017
Type of action: MSCA-ITN-ETN
Start Date: 01/01/18
End Date: 31/12/21
Maximum EU Funding: € 4,076,702.28

 

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21 APRIL 2021