Skip to main content

Analysing Image Compression Using Generative Adversarial Networks

  • Conference paper
  • First Online:
Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 817))

  • 1187 Accesses

Abstract

Most image compression algorithms rely on custom-built encoder–decoder pairs, and they lack flexibility and are differing to the data being compressed. In this paper, we have elaborated on the notion of generative compression by implementing various compression techniques using a generative adversarial network. We have also analysed the compression approaches that are implemented using deep learning. Our experiments are performed on the handwritten digits database and are yielding progressive results with both conditional and quantifiable benchmarks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Raid, A.M., Khedr, W.M., El-dosuky, M.A., Ahmed, W.: JPEG image compression using discrete cosine transform—a survey. CoRR, arXiv:1405.6147 (2014)

  2. Roy, S., Gupta,D.B., Chaudhuri, S.S., Banerjee, P.K.: Studies and implementation of subband coder and decoder of speech signal using Rayleigh distribution. In: Emerging Trends in Computing and Communication, pp. 11–25. Springer (2014)

    Google Scholar 

  3. Delp, E., Mitchell, O.: Image compression using block truncation coding. IEEE Trans. Commun. 27(9), 1335–1342 (1979)

    Article  Google Scholar 

  4. Nasrabadi, N.M., King, R.A.: Image coding using vector quantization: a review. IEEE Trans. Commun. 36(8), 957–971 (1988)

    Article  Google Scholar 

  5. Miar Naimi, H., Salarian, M.: A fast fractal image compression algorithm using predefined values for contrast scaling. CoRR, arXiv:1501.04140 (2015)

  6. Liu, S., Zhang, Z., Qi, L., Ma, M.: A fractal image encoding method based on statistical loss used in agricultural image compression. Multimed. Tools Appl. 75(23), 15525–15536 (2016)

    Article  Google Scholar 

  7. Chakraborty, S., Jalal, A.S., Bhatnagar, C.: An efficient bit plane X-OR algorithm for irreversible image steganography. CoRR, arXiv:1410.3117 (2014)

  8. Choi, M., Tani, J.: Predictive coding for dynamic visual processing: Development of functional hierarchy in a multiple spatio-temporal scales RNN model. CoRR, arXiv:1708.00812 (2017)

  9. Zaman, N., Pippenger, N.: Asymptotic analysis of run-length encoding. CoRR, arXiv:1504.04070 (2015)

  10. Martin, M.B., Bell, A.E.: New image compression techniques using multiwavelets and multiwavelet packets. IEEE Trans. Image Process. 10(4), 500–510 (2001)

    Article  Google Scholar 

  11. Weinberger, M.J., Seroussi, G., Sapiro, G.: The loco-i lossless image compression algorithm: principles and standardization into JPEG-ls. IEEE Trans. Image Process. 9(8), 1309–1324 (2000)

    Article  Google Scholar 

  12. Rane, S.D., Sapiro, G.: Evaluation of JPEG-ls, the new lossless and controlled-lossy still image compression standard, for compression of high-resolution elevation data. IEEE Trans. Geosci. Remote Sens. 39(10), 2298–2306 (2001)

    Article  Google Scholar 

  13. Andrew N.G.: Sparse autoencoder. CS294A Lect. Notes, 72(2011), 1–19 (2011)

    Google Scholar 

  14. Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103. ACM (2008)

    Google Scholar 

  15. Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2528–2535. IEEE (2010)

    Google Scholar 

  16. Sutskever, I., Hinton, G.E., Taylor, G.W.: The recurrent temporal restricted Boltzmann machine. In: Advances in Neural Information Processing Systems, pp. 1601–1608 (2009)

    Google Scholar 

  17. Salakhutdinov, R., Hinton, G.: Deep Boltzmann machines. In: Artificial Intelligence and Statistics, pp. 448–455 (2009)

    Google Scholar 

  18. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks, arXiv:1511.06434 (2015)

  19. Pu, Y., Gan, Z, Henao, R., Yuan, X., Li, C., Stevens, A., Carin, L.: Variational autoencoder for deep learning of images, labels and captions. In: Advances in Neural Information Processing Systems, pp. 2352–2360 (2016)

    Google Scholar 

  20. Gregor, K., Besse, F., Rezende, D.J., Danihelka, I., Wierstra, D.: Towards conceptual compression. In: Advances in Neural Information Processing Systems, pp. 3549–3557 (2016)

    Google Scholar 

  21. Taubman, D., Marcellin, M.: JPEG2000 Image Compression Fundamentals, Standards and Practice: Image Compression Fundamentals, Standards and Practice, volume 642. Springer Science & Business Media (2012)

    Google Scholar 

  22. Santurkar, S., Budden, D.M., Shavit, N.: Generative compression. CoRR, arXiv:1703.01467 (2017)

  23. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)

    Google Scholar 

  24. Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    Google Scholar 

  25. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694–711. Springer (2016)

    Google Scholar 

  26. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rishabh Saxena .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Adate, A., Saxena, R., Gladys Gnana Kiruba, B. (2019). Analysing Image Compression Using Generative Adversarial Networks. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_33

Download citation

Publish with us

Policies and ethics