Skip to main content
SpringerLink
Log in

An Efficient Image Transmission Pipeline for Multimedia Services

  • Conference paper
  • First Online:
MultiMedia Modeling (MMM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12572))

Included in the following conference series:

  • 2439 Accesses

Abstract

The transmitting costs of image data strongly impact the user experience in multimedia services such as Instagram and Twitter. In this paper, we design a novel image transmission pipeline for both efficiently reducing data usage and preserving image quality in those services. Through analyzing the varying features in resized image, we found that high-frequency details achieve a major loss. Based on this impact, we build a series of mechanisms to construct our pipeline: 1) an image resampling mechanism, 2) a high-frequency feature extraction technique, and 3) an image reconstruction method based on the resampled image and its local features. Besides, we also introduce an adaptive binning approach for improving the performance of feature extraction. Finally, we conduct several experiments which show that the proposed pipeline significantly outperforms other baselines in preserving image quality and transmitting time, and is comparable in reducing data usage.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Notes

  1. 1.

    https://www.howtogeek.com/343729/why-your-facebook-photos-look-so-bad-and-what-you-can-do-about-it.

  2. 2.

    https://forum.fujifeed.com/t/uploading-to-instagram-without-compression/233.

  3. 3.

    http://www.enfocus.com/manuals/Extra/PreflightChecks/17/en-us/common/pr/concept/c_aa1035373.html.

  4. 4.

    https://alpha.wallhaven.cc/.

References

  1. Aksoy, Y., et al.: A dataset of flash and ambient illumination pairs from the crowd. In: ECCV (2018)

    Google Scholar 

  2. Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Image coding using wavelet transform. IEEE Trans. Image Process. 1(2), 205–220 (1992)

    Article  Google Scholar 

  3. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  4. Dwork, C., Lei, J.: Differential privacy and robust statistics. In: Proceedings of the Forty-first Annual ACM Symposium on Theory of Computing, STOC 2009, pp. 371–380. ACM, New York (2009). https://doi.org/10.1145/1536414.1536466

  5. Ginesu, G., Pintus, M., Giusto, D.D.: Objective assessment of the webP image coding algorithm. Signal Process. Image Commun. 27(8), 867–874 (2012)

    Article  Google Scholar 

  6. Heuveldop, N., et al.: Ericsson Mobility Report. Ericsson, Stockholm (2017)

    Google Scholar 

  7. Kang, C., Xiang, S., Liao, S., Xu, C., Pan, C.: Learning consistent feature representation for cross-modal multimedia retrieval. IEEE Trans. Multimedia 17(3), 370–381 (2015)

    Article  Google Scholar 

  8. Kopf, J., Shamir, A., Peers, P.: Content-adaptive image downscaling. ACM Trans. Graph. 32(6), 173:1–173:8 (2013). https://doi.org/10.1145/2508363.2508370

    Article  Google Scholar 

  9. Liu, J., He, S., Lau, R.W.H.: \(l_{0}\) -regularized image downscaling. IEEE Trans. Image Process. 27(3), 1076–1085 (2018). https://doi.org/10.1109/TIP.2017.2772838

    Article  MathSciNet  MATH  Google Scholar 

  10. Mairal, J., Elad, M., Sapiro, G.: Sparse representation for color image restoration. IEEE Trans. Image Process. 17(1), 53–69 (2008)

    Article  MathSciNet  Google Scholar 

  11. Mallat, S.: A Wavelet Tour of Signal Processing. Elsevier, Amsterdam (1999)

    MATH  Google Scholar 

  12. Mathur, A., Schlotfeldt, B., Chetty, M.: A mixed-methods study of mobile users’ data usage practices in south africa. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1209–1220. ACM (2015)

    Google Scholar 

  13. Öztireli, A.C., Gross, M.: Perceptually based downscaling of images. ACM Trans. Graph. 34(4), 77:1–77:10 (2015). https://doi.org/10.1145/2766891

    Article  Google Scholar 

  14. Phokeer, A., Densmore, M., Johnson, D., Feamster, N.: A first look at mobile internet use in township communities in South Africa. In: Proceedings of the 7th Annual Symposium on Computing for Development, p. 15. ACM (2016)

    Google Scholar 

  15. Rabbani, M., Jones, P.W.: Digital Image Compression Techniques, vol. 7. SPIE Press, Bellingham (1991)

    Book  Google Scholar 

  16. Salvador, J., Perez-Pellitero, E.: Naive bayes super-resolution forest. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 325–333 (2015)

    Google Scholar 

  17. Shensa, M.J.: The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464–2482 (1992)

    Article  Google Scholar 

  18. Taubman, D., Marcellin, M.: JPEG2000 Image Compression Fundamentals, Standards and Practice. Springer, Heidelberg (2013). https://doi.org/10.1007/978-1-4615-0799-4

    Book  Google Scholar 

  19. Veenadevi, V.: Fractal image compression using quadtree decomposition and huffman coding. Signal Image Process. Int. J. 3, 207–212 (2012). https://doi.org/10.5121/sipij.2012.3215

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shandong University, Qingdao, China

    Zeyu Wang

Authors
  1. Zeyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zeyu Wang .

Editor information

Editors and Affiliations

  1. Charles University, Prague, Czech Republic

    Jakub Lokoč

  2. Charles University, Prague, Czech Republic

    Tomáš Skopal

  3. Klagenfurt University, Klagenfurt, Austria

    Klaus Schoeffmann

  4. CERTH-ITI, Thessaloniki, Greece

    Vasileios Mezaris

  5. Renmin University of China, Beijing, China

    Xirong Li

  6. CERTH-ITI, Thessaloniki, Greece

    Stefanos Vrochidis

  7. Queen Mary University of London, London, UK

    Ioannis Patras

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Z. (2021). An Efficient Image Transmission Pipeline for Multimedia Services. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12572. Springer, Cham. https://doi.org/10.1007/978-3-030-67832-6_57

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-67832-6_57

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67831-9

  • Online ISBN: 978-3-030-67832-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation