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

Compression, Deep learning, Publication

Article accepted – MMSP 2018

31/05/2018 Marco Cagnazzo

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.

Publication