Advertisement

Motion Estimation Enhancement and Data Transmission Issue Over WiMAX Network

  • K. Sai ShivankitaEmail author
  • Nitin Rakesh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 553)

Abstract

High Resolution (HR) image is obtained utilizing set of Low Resolution images using the Super Resolution processes. For motion vector within the frames in Super resolution, the motion estimation algorithms are used. Motion estimation gives a fully designed algorithm in programmable platforms. Highest spatial accuracy is used by the algorithm to adapt the resolution of the image content where it is necessary, that is the border of the moving objects. Binocular vision is the most focused work in reconstruction of image and helps in collecting motion and depth image from defocused images. Shape from Focus (SFF) method is a sequence of images, which is used in the application where high resolution of focused images is given priority. It helps in removing blurred frames and helps in reconstruction. A framework model is proposed in this paper to derive HR images from LR images using motion estimation algorithms and further reconstruction algorithms are implemented using SFF. In this paper, we discuss how to increase the resolution of any image and to reconstruct image and then transferring data to WiMAX network and through WiMAX network this data can be viewed in mobile area as well. The paper proposes with a framework where the model deals with the sequence where we can recreate high resolution image using set of low resolution images and the transferring of data to WiMAX network.

Keywords

IEEE 802.16e Mobile WiMAX Motion estimation Super resolution High Resolution 

References

  1. 1.
    Heiko Schwarz, Detlev Marpe, and Thomas Wiegand. Overview of the scalable video coding extension of the h.264/avc standard. IEEE Trans. Circuits Syst. Video Techn., 17(9), 2007.Google Scholar
  2. 2.
    Patrick Seeling, Martin Reisslein, and Beshan Kulapala. Network performance evaluation using frame size and quality traces of single-layer and two-layer video: A tutorial. IEEE Communications Surveys and Tutorials, 6(1–4):58–78, 2004.Google Scholar
  3. 3.
    S. Sen, J. Dey, J. Kurose, J. Stankovic, and D. Towsley. Streaming CBR transmission of VBR stored video. In SPIE Symposium on Voice Video and Data Communications, 1997.Google Scholar
  4. 4.
    Hayder Radha, Mihaela van der Schaar, and Yingwei Chen. The MPEG-4 fine-grained scalable video coding method for multimedia streaming over IP. IEEE Transactions on Multimedia, 3(1), 2001.Google Scholar
  5. 5.
    M. Elad and A. Feuer, “Restoration of a single super resolution image from several blurred, noisy, and undersampled measured images,” IEEE Trans. Image Process., 1997, vol. 6, no. 12, pp. 1646–1658,.Google Scholar
  6. 6.
    R. Schultz and R.L. Stevenson, “Extraction of high resolution frames from video sequences,” IEEE Trans. Image Process., 996–1011, 1996, vol. 5, no. 6, pp.Google Scholar
  7. 7.
    D. Rajan and S. Chaudhuri, “Simultaneous estimation of super-resolved scene and depth map from low resolution defocused observations,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1102–1117, 2003.Google Scholar
  8. 8.
    D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision (IJCV), 47(1):7–42, 2002.Google Scholar
  9. 9.
    D.A. Forsyth and J. Ponce. Computer Vision: A Modern Approach. Prentice Hall, 2003.Google Scholar
  10. 10.
    O. Nemethova, M. Ries, M. Zavodsky, and M. Rupp. PSNR-based estimation of subjective time-variant video quality for mobiles. In MESAQIN, 2006.Google Scholar
  11. 11.
    S. Das and N. Ahuja. Performance analysis of stereo, vergence, and focus as depth cues for active vision. IEEE Trans Pattern Analysis and Machine Intelligence (PAMI), 17(12):1213–1219, 1995.Google Scholar
  12. 12.
    Ping Li, W.S. Lin, S. Rahardja, X. Lin, X. K. Yang, and Z. G. Li. Geometrically determining leaky bucket parameters for video streaming over constant bit-rate channels. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2004.Google Scholar
  13. 13.
    M. Quartulli and M. Datcu. Bayesian model based city reconstruction from high resolution ISAR data. In IEEE/ISPRS Joint Workshop Remote Sensing and Data Fusion over Urban Areas, 2001.Google Scholar
  14. 14.
    N. Cornelis, B. Leibe, K. Cornelis, and L. Van Gool. 3d city modeling using cognitive loops. In Video Proceedings of CVPR (VPCVPR), 2006.Google Scholar
  15. 15.
    C. Hentschel, et al., Scalable video algorithms and quality-of-service resource management for consumer terminals, in International Conference on Consumer Electronics (ICCE), Los Angeles, CA, June 2001, pp. 338–339.Google Scholar
  16. 16.
    C. Hentschel, R. Braspenning and M. Gabrani, Scalable algorithms for media processing, in International Conference on Image Processing (ICIP), Thessaloniki, Greece, October 2001, pp. 342–345.Google Scholar
  17. 17.
    Mark Kalman, Eckehard G. Steinbach, and Bernd Girod. Adaptive media playout for low-delay video streaming over error-prone channels. IEEE Trans. Circuits Syst. Video Techn., 14(6), 2004.Google Scholar
  18. 18.
    J. Klaue, B. Rathke, and A. Wolisz. Evalvid - A framework for video transmission and quality evaluation”. In International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, 2003.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAmity UniversityNoidaIndia

Personalised recommendations