Neural Multi-scale Image Compression

  • Ken M. NakanishiEmail author
  • Shin-ichi Maeda
  • Takeru Miyato
  • Daisuke Okanohara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11366)


This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder and parallel multi-scale lossless coder. The multi-scale lossy autoencoder extracts the multi-scale image features to quantized variables, and the parallel multi-scale lossless coder enables rapid and accurate lossless coding of the quantized variables via encoding/decoding the variables in parallel. Our proposed model achieves comparable performance to the state-of-the-art model on Kodak and RAISE-1k dataset images, and it encodes a PNG image of size \(768 \times 512\) in 70 ms with a single GPU and a single CPU process and decodes it into a high-fidelity image in approximately 200 ms.

Supplementary material

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Supplementary material 1 (pdf 3261 KB)


  1. 1.
    Agustsson, E., et al.: Soft-to-hard vector quantization for end-to-end learning compressible representations. In: NIPS, pp. 1141–1151 (2017)Google Scholar
  2. 2.
    Ballé, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. In: ICLR (2017)Google Scholar
  3. 3.
    Dang-Nguyen, D.T., Pasquini, C., Conotter, V., Boato, G.: RAISE: a raw images dataset for digital image forensics. In: ACM Multimedia Systems Conference, pp. 219–224 (2015)Google Scholar
  4. 4.
    Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression, vol. 159. Springer, Heidelberg (2012)zbMATHGoogle Scholar
  5. 5.
    Goyal, V.K.: Theoretical foundations of transform coding. IEEE Signal Process. Mag. 18(5), 9–21 (2001)CrossRefGoogle Scholar
  6. 6.
    Johnston, N., et al.: Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks. arXiv preprint arXiv:1703.10114 (2017)
  7. 7.
    Kalkowski, S., Schulze, C., Dengel, A., Borth, D.: Real-time analysis and visualization of the YFCC100M dataset. In: Workshop on Community-Organized Multimodal Mining: Opportunities for Novel Solutions, pp. 25–30 (2015)Google Scholar
  8. 8.
    Li, M., Zuo, W., Gu, S., Zhao, D., Zhang, D.: Learning convolutional networks for content-weighted image compression. arXiv preprint arXiv:1703.10553 (2017)
  9. 9.
    Mentzer, F., Agustsson, E., Tschannen, M., Timofte, R., Van Gool, L.: Conditional probability models for deep image compression. arXiv preprint arXiv:1801.04260 (2018)
  10. 10.
    van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. In: ICML, pp. 1747–1756 (2016)Google Scholar
  11. 11.
    Reed, S., et al.: Parallel multiscale autoregressive density estimation. In: ICML, pp. 2912–2921 (2017)Google Scholar
  12. 12.
    Rippel, O., Bourdev, L.: Real-time adaptive image compression. In: ICML, pp. 2922–2930 (2017)Google Scholar
  13. 13.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  14. 14.
    Theis, L., Shi, W., Cunningham, A., Huszár, F.: Lossy image compression with compressive autoencoders. In: ICLR (2017)Google Scholar
  15. 15.
    Toderici, G., et al.: Variable rate image compression with recurrent neural networks. arXiv preprint arXiv:1511.06085 (2015)
  16. 16.
    Toderici, G., et al.: Full resolution image compression with recurrent neural networks. In: CVPR, pp. 5435–5443 (2017)Google Scholar
  17. 17.
    Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398–1402. IEEE (2004)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Graduate School of ScienceThe University of TokyoBunkyo-kuJapan
  2. 2.Preferred Networks, Inc.Chiyoda-kuJapan

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