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Performance Analysis of Compression Techniques for Multimedia Data

  • Anupama S. BudhewarEmail author
  • Dharmpal D. Doye
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 374)

Abstract

Multimedia compression plays an important role in multimedia communication. In this paper, the problems of compression, quality degradation video and image are discussed. Block matching algorithms compress the image block by block, and it also operates in a temporal manner. Proposed method is a combination of spatio-temporal compression method.It is the combination of intensity and motion estimation. The proposed technique’s compression gains of about90 percent with respective to static JPEG-LS, as it is applied a frame basis. it achieves a good computation complexity and reduces it. An experimental result shows the better performance than existing methods. Due to spatio-temporal combination method quality of degradation is improved compared to existing methods.

Keywords

Motion compensation (MC) Motion estimation (ME) Discrete cosine transform (DCT) Sum of absolute (SAD) Wireless sensor network (WSN) 

References

  1. 1.
    Dai, R., Akyildiz, I.F.: A spatial correlation model for visual information in wireless multimedia sensor networks. IEEE Trans. Multimed. 11(6), 1148–1159 (2009)CrossRefGoogle Scholar
  2. 2.
    Brunello, D., Calvagno, G., Mian, G.A., Rinaldo, R.: Lossless compression of video using temporal information. IEEE Trans. Image Process. 12(2), 132–139 (2003)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Callico, G.M., Lopez, S., Sosa, O., Lopez, J.F., Sarmiento, R.: Analysis of fast block matching motion estimation algorithms for video super-resolution systems. IEEE Trans. Consumer Electron. I 54(3), 1430–1438 (2008)CrossRefGoogle Scholar
  4. 4.
    Chang, N.B., Liu, M.: Optimal competitive algorithms for opportunistic spectrum access. IEEE J. Sel. Areas Commun. 26(7), 1183–1192 (2008)CrossRefGoogle Scholar
  5. 5.
    Choi, C., Jeong, J.: New sorting-based partial distortion elimination algorithm for fast optimal motion estimation. IEEE Trans. Consumer Electron. 55(4), 2335–2340 (2009)CrossRefGoogle Scholar
  6. 6.
    Chung, K.-L., Chang, L.-C.: A new predictive search area approach for fast block motion estimation. IEEE Trans. Image Process. 12(6), 648–652 (2003)CrossRefGoogle Scholar
  7. 7.
    Daribo, I., Florencio, D., Cheung, G.: Arbitrarily shaped motion prediction for depth video compression using arithmetic edge coding. IEEE Trans. Image Process. 23(11), 4696–4708 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gleich, D., Planinšič, P., Gergič, B.: Progressive space frequency quantization for sar data compression. IEEE Trans. Geosci. Remote Sens. 40(1), 3–10 (2002)CrossRefGoogle Scholar
  9. 9.
    Kim, J., Kyung, C.-M.: A lossless embedded compression using significant bit truncation for hd video coding. IEEE Trans. Circuits Syst. Video Technol. 20(6), 848–860 (2010)CrossRefGoogle Scholar
  10. 10.
    Kwok, S.-H., Siu, W.-C., Constantinides, A.G.: Adaptive temporal decimation algorithm with dynamic time window. IEEE Trans. Circuits Syst. Video Technol. 8(1), 104–111 (1998)CrossRefGoogle Scholar
  11. 11.
    Lai, Y.-K., Chen, L.-F., Huang, S.-Y.: Hybrid parallel motion estimation architecture based on fast top-winners search algorithm. IEEE Trans. Consumer Electron. 56(3), 1837–1842 (2010)CrossRefGoogle Scholar
  12. 12.
    Li, H., Li, Z., Wen, C.: Fast mode decision algorithm for inter-frame coding in fully scalable video coding. IEEE Trans. Circuits Syst. Video Technol. 16(7), 889–895 (2006)CrossRefGoogle Scholar
  13. 13.
    Lin, W., Sun, M.-T., Li, H., Chen, Z., Li, W., Zhou, B.: Macroblock classification method for video applications involving motions. IEEE Trans. Broadcast. 58(1), 34–46 (2012)CrossRefGoogle Scholar
  14. 14.
    Lin, Y.-H., Wu, J.-L.: A depth information based fast mode decision algorithm for color plus depth-map 3D videos. IEEE Trans. Broadcast. 57(2), 542–550 (2011)CrossRefGoogle Scholar
  15. 15.
    Liu, B., Zaccarin, A.: New fast algorithms for the estimation of block motion vectors. IEEE Trans. Circuits Syst. Video Technol. 3(2), 148–157 (1993)CrossRefGoogle Scholar
  16. 16.
    Luo, J., Ahmad, I., Liang, Y., Swaminathan, V.: Motion estimation for content adaptive video compression. IEEE Trans. Circuits Syst. Video Technol. 18(7), 900–909 (2008)CrossRefGoogle Scholar
  17. 17.
    Ma, Z., Wang, W., Xu, M., Yu, H.: Advanced screen content coding using color table and index map. IEEE Trans. Image Process. 23(10), 4399–4412 (2014)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Moon, Y.H., Yoon, K.S., Park, S.-T., Shin, I.H.: A new fast encoding algorithm based on an efficient motion estimation process for the scalable video coding standard. IEEE Trans. Multimed. 15(3), 477–484 (2013)CrossRefGoogle Scholar
  19. 19.
    Moshe, Y., Hel-Or, H.: Video block motion estimation based on gray-code kernels. IEEE Trans. Image Process. 18(10), 2243–2254 (2009)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Pudlewski, S., Cen, N., Guan, Z., Melodia, T.: Video transmission over lossy wireless networks: a cross-layer perspective. IEEE J. Sel. Top. Signal Process. 9(1), 6–21 (2015)CrossRefGoogle Scholar
  21. 21.
    Pudlewski, S., Melodia, T.: Compressive video streaming: design and rate-energy-distortion analysis. IEEE Trans. Multimed. 15(8), 2072–2086 (2013)CrossRefGoogle Scholar
  22. 22.
    Shi, Z., Fernando, W., Kondoz, A.: Adaptive direction search algorithms based on motion correlation for block motion estimation. IEEE Trans. Consumer Electron. 57(3), 1354–1361 (2011)CrossRefGoogle Scholar
  23. 23.
    Vo, D.T., Solé, J., Yin, P., Gomila, C., Nguyen, T.Q.: Selective data pruning-based compression using high-order edge-directed interpolation. IEEE Trans. Image Process. 19(2), 399–409 (2010)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Yang, K.H., et al.: A contex-based predictive coder for lossless and near-lossless compression of video. In: Proceedings of the IEEE International Conference on Image Processing, vol. 1, pp. 144–147 (2000)Google Scholar
  25. 25.
    Zhao, G., Ahonen, T., Matas, J., Pietikäinen, M.: Rotation-invariant image and video description with local binary pattern features. IEEE Trans. Image Process. 21(4), 1465–1477 (2012)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Zhu, S., Ma, K.-K.: A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans. Image Process. 9(2), 287–290 (2000)CrossRefGoogle Scholar
  27. 27.
    Budhewar, A.S., Thool, R.C.: Improving performance analysis of multimedia wireless sensor network: a survey. In: IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1441–1448 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringShri Guru Gobind Singhji Institute of Engineering and Technology NandedNandedIndia
  2. 2.Electronics and Telecommunication EngineeringShri Guru Gobind Singhji Institute of Engineering and Technology NandedNandedIndia

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