Fetching the paper…
Reading the bibliography…
We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols.
T. Goblick, “Theoretical limitations on the transmission of data from analog sources,” IEEE Transactions on Information Theory , vol. 11, no. 4, pp. 558–567, October 1965
1965
Earlier work this paper cites.
T. M. Cover and J. A. Thomas, Elements of Information Theory . Wiley-Interscience, 1991
1991
Earlier work this paper cites.
I. Kozintsev and K. Ramchandran, “Robust image transmission over energy-constrained time-varying channels using multiresolution joint source-channel coding,” IEEE Transactions on Signal Processing , vol. 46, no. 4, pp. 1012–1026, April 1998
1998
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli et al. , “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
F. Zhai, Y. Eisenberg, and A. K. Katsaggelos, “Joint source-channel coding for video communications,” in Handbook of Image and Video Processing , 2nd ed., A. Bovik, Ed. Burlington: Academic Press, 2005
2005
Earlier work this paper cites.
N. Thomos, N. V. Boulgouris, and M. G. Strintzis, “Optimized transmission of JPEG2000 streams over wireless channels,” IEEE Trans. on Image Processing , vol. 15, no. 1, pp. 54–67, Jan 2006
2006
Earlier work this paper cites.
A. G. Fabregas, A. Martinez, and G. Caire, “Bit-interleaved coded modulation,” Foundations and Trends in Communications and Information Theory , vol. 5, no. 1-2, pp. 1–153, 2008
2008
Earlier work this paper cites.
D. Gunduz and E. Erkip, “Joint source-channel codes for MIMO block-fading channels,” IEEE Trans. on Information Theory , vol. 54, no. 1, pp. 116–134, Jan 2008
2008
Earlier work this paper cites.
T. Richardson and R. Urbanke, Modern Coding Theory . New York, NY, USA: Cambridge University Press, 2008
2008
Earlier work this paper cites.
Y. Bengio, “Learning deep architectures for AI,” Found. and Trends in Machine Learning , vol. 2, no. 1, pp. 1–127, Jan. 2009
2009
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” University of Toronto, Tech. Rep., 2009
2009
Earlier work this paper cites.
J. Deng et al. , “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR09 , 2009
2009
Earlier work this paper cites.
S. Jakubczak and D. Katabi, “SoftCast: Clean-slate scalable wireless video,” in Proc. of the 48th IEEE Annual Allerton Conf. on Communication, Control, and Computing , Illinois,USA, Sept. 2010, pp. 530–533
2010
Earlier work this paper cites.
Y. Polyanskiy, H. V. Poor, and S. Verdu, “Channel coding rate in the finite blocklength regime,” IEEE Transactions on Information Theory , vol. 56, no. 5, pp. 2307–2359, May 2010
2010
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” arXiv:1412.6980 [cs.LG] , 2014
2014
Cited alongside, same era.
2015
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning . MIT Press, 2016
2016
Cited alongside, same era.
T. J. O’Shea, K. Karra, and T. C. Clancy, “Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention,” in Proc. of IEEE Int. Symp. on Signal Processing and Information Technology (ISSPIT) , Dec. 2016, pp. 223–228
H. Kim et al. , “Communication algorithms via deep learning,” in Proc. of Int. Conf. on Learning Representations (ICLR) , 2018
2018
Closest in time.
E. Nachmani et al. , “Deep learning methods for improved decoding of linear codes,” IEEE Journal of Selected Topics in Signal Processing , vol. 12, no. 1, pp. 119–131, Feb 2018
2018
Closest in time.
A. Caciularu and D. Burshtein, “Blind channel equalization using variational autoencoders,” in Proc. IEEE Int. Conf. on Comms. Workshops, Kansas City, MO , May 2018, pp. 1–6
2018
Closest in time.
A. Felix, S. Cammerer, S. Dorner, J. Hoydis, and S. ten Brink, “OFDM autoencoder for end-to-end learning of communications systems,” in Proc. IEEE Int. Workshop Signal Proc. Adv. Wireless Commun. (SPAWC) , Jun. 2018
2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
J. Balle, V. Laparra, and E. P. Simoncelli, “End-to-end optimized image compression,” in Proc. of Int. Conf. on Learning Representations (ICLR) , Apr. 2017, pp. 1–27
2017
Cited alongside, same era.
L. Theis, W. Shi, A. Cunnigham, and F. Huszár, “Lossy image compression with compressive autoencoders,” in Proc. of the Int. Conf. on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
O. Rippel and L. Bourdev, “Real-time adaptive image compression,” in Proc. Int. Conf. on Machine Learning (ICML) , vol. 70, Aug. 2017, pp. 2922–2930
2017
Cited alongside, same era.
T. O’Shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Transactions on Cognitive Communications and Networking , vol. 3, no. 4, pp. 563–575, Dec 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
P. T. Chiou, Y. Sun, and G. S. Young, “A complexity analysis of the JPEG image compression algorithm,” in Proc. of 9th Computer Science and Electronic Engineering (CEEC) , Sep. 2017, pp. 65–70
2017
Cited alongside, same era.
2018
Closest in time.
N. Farsad, M. Rao, and A. Goldsmith, “Deep learning for joint source-channel coding of text,” in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP) , Apr. 2018
2018
Closest in time.
R. Zarcone et al. , “Joint source-channel coding with neural networks for analog data compression and storage,” in 2018 Data Compression Conference , March 2018, pp. 147–156
2018
Closest in time.
2018
Closest in time.
T. Tung and D. Gunduz, “Sparsecast: Hybrid digital-analog wireless image transmission exploiting frequency-domain sparsity,” IEEE Communications Letters , vol. 22, no. 12, pp. 2451–2454, Dec 2018
2018
Closest in time.
D. Alexandre, C.-P. Chang, W.-H. Peng, and H.-M. Hang, “An autoencoder-based learned image compressor: Description of challenge proposal by nctu,” in IEEE Conf. Comp. Vision and Pattern Recog. Works. , Jun. 2018
2018
Closest in time.
N. Johnston et al. , “Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Closest in time.
A. Ignatov et al. , “AI benchmark: Running deep neural networks on android smartphones,” in Computer Vision – ECCV 2018 Workshops , L. Leal-Taixé and S. Roth, Eds. Cham: Springer, 2019, pp. 288–314
2019
Closest in time.