Advertisement

Image Retargeting Using Dynamic Load Balancing-Based Parallel Architecture

  • Ganesh V. PatilEmail author
  • Santosh L. Deshpande
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)

Abstract

Nowadays, enormously expanding use of mobile gadgets for capturing images is getting overwhelming response. This fact results in a tremendously increasing usage of digital images. To maintain the quality of vastly pervading digital images on variable sized display contraptions becomes a pensive task for a Web administrator. We are providing a three-leveled image retargeting approach on a parallel architecture with ranking-based dynamic load balancing (RBDLB). Image retargeting is both computational and memory intensive task. Static load balancing cannot offer equity to image retargeting errand as incoming image jobs are required to be processed at dynamic time. The motive of thought process of this undertaking is to provide a good response time and efficient resource utilization in a task of image retargeting without compromising quality of image.

Keywords

Image retargeting Image resizing Quantization Compression Dynamic load balancing 
This is a preview of subscription content, log in to check access.

References

  1. 1.
  2. 2.
    Liang, Y., Liu, Y.-J., Gutierrez, D.: Objective quality prediction of image retargeting algorithms. IEEE Trans. Visual Comput. Graph. 23(2), 1–13 (2017)CrossRefGoogle Scholar
  3. 3.
    Zhang, Y., Ngan, K.N., Ma, L., Li, H.: Objective quality assessment of image retargeting by incorporating fidelity measures and inconsistency detection. IEEE Trans. Image Process., 1–14. (This article has been accepted for publication but not yet published)Google Scholar
  4. 4.
    Lin, Y., Niu, Y., Lin, J., Zhang, H.: Accumulative energy based seam carving for image resizing. In: Presented in 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pp. 366–371 (2016)Google Scholar
  5. 5.
    Avidan, S., Shamir, A.: Seam carving for content aware image resizing. ACM Trans. Graph. 27(3), 1–9 (2008)Google Scholar
  6. 6.
    Hua, S., Wei, H., Su, T.: Fast image retargeting based on strip dividing and resizing. J. Syst. Eng. Electron. 25(6), 1072–1081 (2014)CrossRefGoogle Scholar
  7. 7.
    Zhu, L., Chen, Z.: Fast genetic multi-operator image retargeting. In: Presented in IEEE Conference on Visual Communications and Image Processing (VCIP), November 2016Google Scholar
  8. 8.
    Zhang, L., Li, K., Ou, Z., Wang, F.: Seam warping: a new approach for image retargeting for small displays (2015)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Image Quantization. https://en.wikipedia.org/wiki/Quantization_(image_processing). Accessed 6 Nov 2017
  11. 11.
    Image Compression (2017). https://en.wikipedia.org/wiki/Image_compression. Accessed 6 Nov 2017
  12. 12.
    Image Resizing. http://www.imagemagick.org/Usage/resize/. Accessed 6 Nov 2017
  13. 13.
    Pngquant. https://pngquant.org. Accessed 6 Nov 2017
  14. 14.
    Advpng. http://www.advancemame.it/doc-advpng.html. Accessed 6 Nov 2017
  15. 15.
    Image Magicks Resize. http://www.imagemagick.org/Usage/resize/. Accessed 6 Nov 2017
  16. 16.
    Grafana. https://grafana.com. Accessed 6 Nov 2017
  17. 17.
    Patil, G., Deshpande, S.L.: Distributed rendering system for 3D animation with blender. In: IEEE International Conference on Advances in Electronics, Communication and Computer Technology, pp. 92–98, December 2016Google Scholar
  18. 18.
    Li, M., Baker, M.: The Grid Core Technologies, Chapter 6, p. 252. WileyGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Vishveshwaraya Technical University BelgaumBelgaumIndia

Personalised recommendations

Cite paper

Buy options