I presented the team activity report in the context of the HCERES evaluation of the lab.
[slideshare id=131600499&doc=mmhceres-190213082651]
All posts by Marco Cagnazzo
Promotion
I have been promoted to Full Professor (starting from Dec 1st 2018)
Poster MMSP 2018
“Quality assessment of deep-learning based image compression”
[slideshare id=132238942&doc=postermmsp2018web-190218143634&type=d]
Deep-learning based compression at IDS research day
[slideshare id=118691244&doc=ids2018-181008130752]
(Français) TP IMA 208
Best student paper award
Our paper “View-dependent compression of digital hologram based on matching pursuit”, byA. El Rhammad, P. Gioia, A. Gilles, M. Cagnazzo, B. Pesquet-Popescu, published in SPIE Photonics Europe, vol. 10679 (Avril 2018, Strasbourg, France) has been awarded by the Best Student Paper Award. Congratulations to Anas !
http://spie.org/conferences-and-exhibitions/photonics-europe/best-student-paper-awards
Article accepted – MMSP 2018
Our article on quality assessment of compressed images with deep learning techniques has been accepted in the IEEE Multimedia Signal Processing 2018 (MMSP) conference [1].
In this article we use two of the most recent compression methods based on DL, developed respectively by Ballé et al. [2] and by Toderici et al. [3]. The images compressed with these methods were evaluated by a panel of around twenty people. We also considered images compressed with “classical” techniques (HEVC-INTRA (BPG) and JPEG2000). We found that the subjective quality is often better than JPEG2000, and in any case very close. On the other hand, BPG still has better results on average, even if on certain images the method [3] is the best one.
RDV on [1] for more details! (The PDF of this article will be available soon).
[1] G. Valenzise, A. Purica, V. Hulusic, M. Cagnazzo. “Quality Assessment of Deep-Learning-Based Image Compression”. To appear in IEEE Multimedia Signal Processing Workshop, 2018.
[2] J. Ballé, V. Laparra, and E. P. Simoncelli, “End-to-end optimized image compression,” in Int. Conf. on Learning Representations (ICLR), Toulon, France, Apr. 2017.
[3] Toderici G., Vincent D., Johnston N., Hwang S., Minnen D, Shor J., Covell M., “Full resolution image compression with recurrent neural networks,” in IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, USA, Jul. 2017, pp. 5435-5443.
(Français) Article sur la compression d’hologrammes numériques accepté
(Français) SD-TSIA205: Débruitage par ondelettes
Newcomers
Chiara Scolavino and Ciro Assediato join the team for their master degree final stage.