A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, 1991.
DOI : 10.1007/978-1-4615-3626-0

N. Ahmed, T. Natarajan, and K. R. Rao, Discrete Cosine Transform, IEEE Transactions on Computers, vol.23, issue.1, pp.90-93, 1974.
DOI : 10.1109/T-C.1974.223784

S. Mallat, A wavelet tour of signal processing. Academic press, 1999.

I. Tosic and P. Frossard, Dictionary Learning, IEEE Signal Processing Magazine, vol.28, issue.2, pp.27-38, 2011.
DOI : 10.1109/MSP.2010.939537

L. Theis, W. Shi, A. Cunningham, and F. Huszár, Lossy image compression with compressive autoencoders, Int. Conf. on Learning Representations (ICLR), 2017.

E. Agustsson, F. Mentzer, M. Tschannen, L. Cavigelli, R. Timofte et al., Soft-to-hard vector quantization for endto-end learning compressible representations, Advances in Neural Information Processing Systems, pp.1141-1151, 2017.

D. Minnen, G. Toderici, M. Covell, T. Chinen, N. Johnston et al., Spatially adaptive image compression using a tiled deep network, 2017 IEEE International Conference on Image Processing (ICIP), pp.2796-2800, 2017.
DOI : 10.1109/ICIP.2017.8296792

O. Rippel and L. Bourdev, Real-time adaptive image compression, 2017.

J. Ballé, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, Variational image compression with a scale hyperprior, Int. Conf. on Learning Representations (ICLR), 2018.

E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L. Van-gool, Generative adversarial networks for extreme learned image compression, 2018.

D. P. Kingma and M. Welling, Auto-encoding variational bayes, Int. Conf. on Learning Representations (ICLR), 2014.

I. Goodfellow, A. Courville, Y. Bengio, and D. Learning, , 2016.

S. Santurkar, D. Budden, and N. Shavit, Generative compression, 2017.

J. Ballé, V. Laparra, and E. P. Simoncelli, End-to-end optimized image compression, Int. Conf. on Learning Representations (ICLR), 2017.

G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen et al., Full Resolution Image Compression with Recurrent Neural Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5435-5443, 2017.
DOI : 10.1109/CVPR.2017.577

J. Froehlich, S. Grandinetti, B. Eberhardt, S. Walter, A. Schilling et al., Creating cinematic wide gamut HDR-video for the evaluation of tone mapping operators and HDR-displays, Digital Photography X International Society for Optics and Photonics, p.90230, 2014.

M. D. Fairchild, The HDR photographic survey, Color and Imaging Conference, pp.233-238, 2007.

R. Mantiuk, S. Daly, and L. Kerofsky, Display adaptive tone mapping, in ACM Transactions on Graphics (TOG), vol.27, issue.3, p.68, 2008.

V. Hulusic, K. Debattista, G. Valenzise, and F. Dufaux, A model of perceived dynamic range for HDR images, Signal Processing: Image Communication, pp.26-39
DOI : 10.1016/j.image.2016.11.005

URL : https://hal.archives-ouvertes.fr/hal-01441630

I. , Methodology for the subjective assessment of the quality of television pictures, ITU-R Recommendation BT, pp.500-513, 2012.

I. , Methods, metrics and procedures for statistical evaluation, qualification and comparison of objective quality prediction models, 2012.

H. R. Sheikh, M. F. Sabir, and A. C. Bovik, A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms, IEEE Transactions on Image Processing, vol.15, issue.11, pp.3440-3451, 2006.
DOI : 10.1109/TIP.2006.881959

N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh et al., Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks, 2017.

A. Mittal, R. Soundararajan, and A. C. Bovik, Making a ???Completely Blind??? Image Quality Analyzer, IEEE Signal Processing Letters, vol.20, issue.3, pp.209-212, 2013.
DOI : 10.1109/LSP.2012.2227726