Visual Quality - Deep Learning - Just noticeable differences - Data Analysis
In this seminar we cover topics relevant to the current Bachelor, Master, and PhD projects in the Multimedia Signal Processing Group.
- Image and video quality assessment (IQA/VQA): Nowadays, multimedia data are ubiquitous. You take pictures and videos with your camera and camcorders everyday. Have you thought about how you can judge the perceptual quality of the pictures and videos you made and how good your camera/camcorder is? In this seminar, we will discuss algorithms for image and video quality assessment. Topics will be related to our ongoing VQA Project in the SFB TRR 161.
- Recently deep learning has been applied to the problem of IQA/VQA and we will cover several approaches in the seminar.
- We will read and discuss papers about "Justnoticeable differences" when comparing an original image/video with a version that has been slightly distorted or enhanced.
- The seminar is a good preparation for a student project in multimedia signal processing that can one or more of the following: quality assessment algorithms, crowdsourcing perceptual quality, eye tracking, data analysis.
Requirements for obtaining ECTS credits for this seminar are:
- regular attendance at the weekly seminar
- self-study of one or more scientific papers on selected topics
- oral presentation of 45 minutes including discussion
- written essay on the subject of the presentation (about 10-20 pages, typeset, electronic submission as one pdf)
Students with a keen interest in multimedia signal processing are welcome to participate in the seminar. Especially those that have previously taken relevant courses like, e.g., Digital Signal Processing, Eye Tracking Theory and Practice, Machine Learning Using MATLAB, Deep Learning Programming, Image Processing.
Topics and Literature
Topic: Learning from rankings
- Liu, X., van de Weijer, J. and Bagdanov, A.D., 2017. Rank-IQA: Learning from rankings for no-reference image quality assessment. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1040-1049). Available online. Supplementary materials available online. Paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_RankIQA_ Learning_From_ICCV_2017_paper.pdf
Topic: Image augmentations for database enhancement
- Zhu, Y., Zhai, G., Zhu, W. and Zhou, J., 2018, May. An Image Augmentation Method for Quality Assessment Database. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS). Abstract available online at IEEE. Will get the paper asap
Topic: Artifact detection in video
- Goodall, T.R., Bovik, A., Detecting and Mapping Video Impairments, IEEE Transactions on Image Processing 28(6), June 2019. Thesis available online: Goodall, T.R., 2018. Inspection and evaluation of artifacts in digital video sources (Doctoral dissertation). Also the software is available online and should be tried out for the seminar. Thesis: https://repositories.lib.utexas.edu/handle/2152/68147
Topic: Just noticeable differences I
- Katsigiannis, S., Scovell, J., Ramzan, N., Janowski, L., Corriveau, P., Saad, M.A. and Van Wallendael, G., 2018. Interpreting MOS scores, when can users see a difference? Understanding user experience differences for photo quality. Quality and User Experience, 3(1), p.6. Paper: https://link.springer.com/article/10.1007/s41233-018-0019-8
Topic: Just noticeable differences II
- Wu, J., Shi, G., Lin, W., Liu, A., Qi, F. (2013). Just noticeable difference estimation for images with free-energy principle. IEEE Transactions on Multimedia, 15(7), 1705-1710. Talk should also consider the three methods of Yang, Liu, Zhang. Paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.980&rep=rep1&type=pdf
Topic: Weighted mean square errors
- Hu, S., Jin, L., Wang, H., Zhang, Y., Kwong, S. and Kuo, C.C.J., 2017. Objective video quality assessment based on perceptually weighted mean squared error. IEEE Transactions on Circuits and Systems for Video Technology, 27(9), pp.1844-1855. Paper: http://codec.siat.ac.cn/downloads/publications/47.pdf
- Xue, W., Mou, X., Zhang, L. and Feng, X., 2013. Perceptual fidelity aware mean squared error. In Proceedings of the IEEE International Conference on Computer Vision (pp. 705-712). Paper: https://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Xue_Perceptual_Fidelity_Aware_2013_ICCV_paper.pdf