1. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015).https://www.tensorflow.org/, software available from tensorflow.org

2. Agustsson, E., et al.: Soft-to-hard vector quantization for end-to-end learning compressible representations. Adv. Neural Inf. Process. Syst.30, 1141–1151 (2017)

   Google Scholar 

3. Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., Van Gool, L.: Generative adversarial networks for extreme learned image compression. arXiv preprintarXiv:1804.02958 (2018)

4. Ballé, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. In: International Conference on Learning Representations (ICLR) (2017)

   Google Scholar 

5. Bellard, F.: BPG image format (2014).https://bellard.org/bpg/

6. Bruna, J., Mallat, S.: Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell.35(8), 1872–1886 (2013)

   Article  Google Scholar 

7. Bruna, J., Sprechmann, P., LeCun, Y.: Super-resolution with deep convolutional sufficient statistics. In: International Conference on Learning Representations (ICLR), May 2016

   Google Scholar 

8. Chinen, T., et al.: Towards a semantic perceptual image metric. In: 2018 25th IEEE International Conference on Image Processing (ICIP), October 2018

   Google Scholar 

9. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017

   Google Scholar 

10. Chollet, F., et al.: Keras (2015).https://keras.io

11. Dodge, S., Karam, L.: Understanding how image quality affects deep neural networks. In: 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–6 (2016)

    Google Scholar 

12. Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. Adv. Neural Inf. Process. Syst.29, 658–666 (2016)

    Google Scholar 

13. Galteri, L., Seidenari, L., Bertini, M., Del Bimbo, A.: Deep generative adversarial compression artifact removal. In: The IEEE International Conference on Computer Vision (ICCV), October 2017

    Google Scholar 

14. Gatys, L., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 262–270 (2015)

    Google Scholar 

15. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

    Google Scholar 

16. Google: Webp image format (2015).https://developers.google.com/speed/webp/. Accessed 17 Mar 2019

17. Gueguen, L., Sergeev, A., Kadlec, B., Liu, R., Yosinski, J.: Faster neural networks straight from jpeg. Adv. Neural Inf. Process. Syst.31, 3933–3944 (2018)

    Google Scholar 

18. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

    Google Scholar 

19. Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications (2017).arXiv:1704.04861

20. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017

    Google Scholar 

21. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016).https://doi.org/10.1007/978-3-319-46475-6_43

    Chapter  Google Scholar 

22. Johnston, N., 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

    Google Scholar 

23. Khosla, A., Jayadevaprakash, N., Yao, B., Fei-Fei, L.: Novel dataset for fine-grained image categorization. In: First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, June 2011

    Google Scholar 

24. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR), May 2015

    Google Scholar 

25. Kodak, E.: Kodak lossless true color image suite (PhotoCD PCD0992)

    Google Scholar 

26. LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems, pp. 253–256 (2010)

    Google Scholar 

27. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017

    Google Scholar 

28. Liu, H., Chen, T., Shen, Q., Yue, T., Ma, Z.: Deep image compression via end-to-end learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2018

    Google Scholar 

29. Liu, Z., et al.: DeepN-JPEG: a deep neural network favorable JPEG-based image compression framework. In: 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), pp. 1–6 (2018)

    Google Scholar 

30. Liu, Z., et al.: Machine vision guided 3D medical image compression for efficient transmission and accurate segmentation in the clouds. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019

    Google Scholar 

31. Mallat, S.: Understanding deep convolutional networks. Philos. Trans. Roy. Soc. A374(2065) (2016)

    Google Scholar 

32. Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., Van Gool, L.: Conditional probability models for deep image compression. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018

    Google Scholar 

33. Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., Van Gool, L.: Practical full resolution learned lossless image compression. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019

    Google Scholar 

34. Odena, A., Dumoulin, V., Olah, C.: Deconvolution and checkerboard artifacts. Distill (2016).http://distill.pub/2016/deconv-checkerboard/

35. Rippel, O., Bourdev, L.: Real-time adaptive image compression. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2922–2930, August 2017

    Google Scholar 

36. Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis.115(3), 211–252 (2015)

    Article MathSciNet  Google Scholar 

37. Santurkar, S., Budden, D., Shavit, N.: Generative compression. In: 2018 Picture Coding Symposium (PCS), pp. 258–262 (2018)

    Google Scholar 

38. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR), May 2015

    Google Scholar 

39. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI 2017, pp. 4278–4284. AAAI Press (2017)

    Google Scholar 

40. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

    Google Scholar 

41. Theis, L., Shi, W., Cunningham, A., Huszár, F.: Lossy image compression with compressive autoencoders. In: International Conference on Learning Representations (ICLR) (2017)

    Google Scholar 

42. Toderici, G., et al.: Variable rate image compression with recurrent neural networks. In: International Conference on Learning Representations (ICLR) (2016)

    Google Scholar 

43. Toderici, G., et al.: Full resolution image compression with recurrent neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5435–5443, July 2017

    Google Scholar 

44. Torfason, R., Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., Gool, L.V.: Towards image understanding from deep compression without decoding. In: International Conference on Learning Representations (ICLR), April 2018

    Google Scholar 

45. Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD Birds-200-2011 Dataset. Technical report. CNS-TR-2011-001, California Institute of Technology (2011)

    Google Scholar 

46. Wallace, G.K.: The jpeg still picture compression standard. IEEE Trans. Cons. Electron.38(1), xviii-xxxiv (1992)

    Google Scholar 

47. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398–1402 (2003)

    Google Scholar 

48. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018

    Google Scholar 

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