Image Compression Using Deep Learning Based Multi-structure Feature Map and K-Means Clustering

  • Gyanendra VermaEmail author
  • Anant KumarEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1240)


Image compression play significant role in the data transfer and storage. Recently, deep learning has achieved tremendous success in various domain of image processing. In this paper, we propose a multi-structure Feature map-based Deep Learning approach with K-means Clustering for image compression. We first use a modified CNN to select a multi-structured region of interest MS-ROI feature map by using several stacked of convolution layers then compress the image by integrating MS-ROI map with K-means. We can establish through experimental results that the proposed approach perform better compared to traditional K-means clustering approach.


K-Means Image compression Deep learning 


  1. 1.
    Schubert, E., Sander, J., Ester, M., Kriegel, H.-P., Xu, X.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. 42(3), 21 (2017). Article 19Google Scholar
  2. 2.
    Na, S., Xumin, L., Yong, G.: An improved k-means clustering algorithm. In: Third International Symposium on Intelligent Information Technology and Security Informatics. IEEE (2010)Google Scholar
  3. 3.
    Paek, J., Ko, J.: K-means clustering-based data compression scheme for wireless imaging sensor networks. IEEE Syst. J. 11(4) (2017)Google Scholar
  4. 4.
    Wang, X., Wang, L.M., Qiao, Y.: A comparative study of encoding, pooling and normalization methods for action recognition. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7726, pp. 572–585. Springer, Heidelberg (2013). Scholar
  5. 5.
    Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning Deep Features for Discriminative Localization, arXiv:1512.04150v1 [cs.CV], 14 Dec 2015
  6. 6.
    Prakash, A., Moran, N., Garber, S., DiLillo, A., Storer, J.: Semantic Perceptual Image Compression using Deep Convolution Networks, arXiv:1612.08712v2 [cs.MM], 29 Mar 2017
  7. 7.
    Qassim, H., Verma, A., Feinzimer, D.: Compressed residual-VGG16 CNN model for big data places image recognition. IEEE (2018)Google Scholar
  8. 8.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)Google Scholar
  9. 9.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  10. 10.
    Chrabaszcz, P., Loshchilov, I., Frank, H.: A downsampled variant of ImageNet as an alternative to the CIFAR datasets. arXiv preprint arXiv:1707.08819 (2017)
  11. 11.
    Horé, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 International Conference on Pattern Recognition (2010)Google Scholar
  12. 12.
    Raid, A.M., Khedr, W.M., El-dosuky, M.A., Ahmed, W.: Jpeg image compression using discrete cosine transform - a survey. Int. J. Comput. Sci. Eng. Surv. (IJCSES) 5(2) (2014)Google Scholar
  13. 13.
    Rabbani, M., Joshi, R.: An overview of the JPEG 2000 still image compression standard. Sig. Process.: Image Commun. 17, 73–84 (2002)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Institute of Technology, KurukshetraKurukshetraIndia

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