Fetching the paper…
Reading the bibliography…
Image compression is a widely used technique to reduce the spatial redundancy in images.
I. H. Witten, R. M. Neal, and J. G. Cleary, “Arithmetic coding for data compression,” Communications of the ACM , vol. 30, no. 6, pp. 520–540, 1987
1987
Earlier work this paper cites.
G. K. Wallace, “The jpeg still picture compression standard,” IEEE Transactions on Consumer Electronics , vol. 38, no. 1, pp. xviii–xxxiv, 1992
1992
Earlier work this paper cites.
A. Skodras, C. Christopoulos, and T. Ebrahimi, “The jpeg 2000 still image compression standard,” IEEE Signal Processing Magazine , vol. 18, no. 5, pp. 36–58, 2001
2001
Earlier work this paper cites.
Z. Wang, E. Simoncelli, A. Bovik et al. , “Multi-scale structural similarity for image quality assessment,” in ASILOMAR CONFERENCE ON SIGNALS SYSTEMS AND COMPUTERS , vol. 2. IEEE; 1998, 2003, pp. 1398–1402
2003
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814
2010
Earlier work this paper cites.
G. J. Sullivan, J.-R. Ohm, W.-J. Han, T. Wiegand et al. , “Overview of the high efficiency video coding(hevc) standard,” TCSVT , vol. 22, no. 12, pp. 1649–1668, 2012
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
G. Toderici, S. M. O’Malley, S. J. Hwang, D. Vincent, D. Minnen, S. Baluja, M. Covell, and R. Sukthankar, “Variable rate image compression with recurrent neural networks,” in 4th International Conference on Learning Representations, ICLR , 2016
2016
Earlier work this paper cites.
G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, and M. Covell, “Full resolution image compression with recurrent neural networks.” in CVPR , 2017, pp. 5435–5443
2017
Earlier work this paper cites.
J. Ballé, V. Laparra, and E. P. Simoncelli, “End-to-end optimized image compression,” in 5th International Conference on Learning Representations, ICLR , 2017
2017
Earlier work this paper cites.
L. Theis, W. Shi, A. Cunningham, and F. Huszár, “Lossy image compression with compressive autoencoders,” in 5th International Conference on Learning Representations, ICLR , 2017
2017
Earlier work this paper cites.
E. Agustsson, F. Mentzer, M. Tschannen, L. Cavigelli, R. Timofte, L. Benini, and L. V. Gool, “Soft-to-hard vector quantization for end-to-end learning compressible representations,” in NIPS , 2017, pp. 1141–1151
2017
Earlier work this paper cites.
O. Rippel and L. Bourdev, “Real-time adaptive image compression,” in ICML , 2017
2017
Earlier work this paper cites.
M. H. Baig, V. Koltun, and L. Torresani, “Learning to inpaint for image compression,” in NIPS , 2017, pp. 1246–1255
2017
Cited alongside, same era.
B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced deep residual networks for single image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2017, pp. 136–144
2017
Cited alongside, same era.
A. Ranjan and M. J. Black, “Optical flow estimation using a spatial pyramid network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017
2017
Cited alongside, same era.
D. Minnen, J. Ballé, and G. D. Toderici, “Joint autoregressive and hierarchical priors for learned image compression,” in Advances in Neural Information Processing Systems , 2018, pp. 10 771–10 780
2018
Cited alongside, same era.
“Webp.” https://developers.google.com/speed/webp/ , accessed: 2018-10-30
X. Wang, R. Girshick, A. Gupta, and K. He, “Non-local neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7794–7803
2018
Later among the works it cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Later among the works it cites.
“x265 hevc encoder / h.265 video codec.” http://x265.org , accessed: 2018-10-30
2018
Later among the works it cites.
G. Lu, W. Ouyang, D. Xu, X. Zhang, C. Cai, and Z. Gao, “DVC: An end-to-end deep video compression framework,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,CVPR , 2019, pp. 11 006–11 015
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
“F. bellard, bpg image format.” http://bellard.org/bpg/ , accessed: 2018-10-30
2018
Cited alongside, same era.
J. Ballé, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Variational image compression with a scale hyperprior,” in 6th International Conference on Learning Representations, ICLR , 2018
2018
Cited alongside, same era.
M. Li, W. Zuo, S. Gu, D. Zhao, and D. Zhang, “Learning convolutional networks for content-weighted image compression,” in CVPR , June 2018
2018
Cited alongside, same era.
F. Mentzer, E. Agustsson, M. Tschannen, R. Timofte, and L. Van Gool, “Conditional probability models for deep image compression,” in CVPR , no. 2, 2018, p. 3
2018
Cited alongside, same era.
2018
Cited alongside, same era.
N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. Jin Hwang, J. Shor, and G. Toderici, “Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks,” in CVPR , June 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Later among the works it cites.
Y. Choi, M. El-Khamy, and J. Lee, “Variable rate deep image compression with a conditional autoencoder,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 3146–3154
2019
Later among the works it cites.
Z. Cheng, H. Sun, M. Takeuchi, and J. Katto, “Learning image and video compression through spatial-temporal energy compaction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR , 2019, pp. 10 071–10 080
2019
Later among the works it cites.
A. Habibian, T. v. Rozendaal, J. M. Tomczak, and T. S. Cohen, “Video compression with rate-distortion autoencoders,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 7033–7042
2019
Later among the works it cites.
A. Djelouah, J. Campos, S. Schaub-Meyer, and C. Schroers, “Neural inter-frame compression for video coding,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 6421–6429
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
T. Xue, B. Chen, J. Wu, D. Wei, and W. T. Freeman, “Video enhancement with task-oriented flow,” International Journal of Computer Vision, IJCV , vol. 127, no. 8, pp. 1106–1125, 2019
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 . Curran Associates, Inc., 2019, pp. 8024–8035. [Online]. Available: http://papers.nips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
2019
Later among the works it cites.