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An Efficient Video Compression System Based on LSK Encoder

  • S. Anantha Padmanabhan
  • S. Chandramathi
Part of the Communications in Computer and Information Science book series (CCIS, volume 250)

Abstract

Video compression means reducing the size of data in order to represent the digital video images. The main goal of our research is to develop a robust video compression system. To begin with, wavelet decomposition is applied to the I-frame and using the Listless SPECK (LSK) algorithm, the resulting coefficients are quantized. Subsequently, an Adaptive Rood Search with Spatio-Temporal Correlation technique (ARS-ST) is employed for motion estimation. Finally, the difference between the original P-frame and the predicted P-frame is computed, which is referred as the residual. The proposed video compression technique is assessed by several videos and the effectiveness of compression is evaluated by measuring the PSNR values for the compression results.

Keywords

Video compression Listless SPECK (LSK) motion estimation Adaptive Rood Search with Spatio temporal Correlation (ARS-ST) Motion Vector Prediction 

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References

  1. 1.
    Danyali, H., Mertins, A.: A 3-D virtual SPIHT for scalable very low bit rate embedded video compression. In: Proc. of the 6th international symposium on Digital Signal Processing for communication Systems (DSPCS 2002), pp. 123–127 (2002)Google Scholar
  2. 2.
    Sullivan, G.J., Wiegand, T.: Video Compression-From Concepts to the H.264/AVC Standard. Proc. of the IEEE 93(1), 18–31 (2005)CrossRefGoogle Scholar
  3. 3.
    Leontaris, A., Cosman, P.C.: Video Compression with Intra/Inter Mode Switching and a Dual Frame Buffer. In: Proc. of the Data Compression Conference (DCC 2003), pp. 63–72 (2003)Google Scholar
  4. 4.
    Thamarai, M., Shanmugalakshmi, R.: Video Coding Technique Using Swarm Intelligence in 3-D Dual Tree Complex Wavelet Transform. In: Proc. of the Second International Conference on Machine Learning and Computing (ICMLC), Bangalore, pp. 174–178 (2010)Google Scholar
  5. 5.
    Abomhara, M., Khalifa, O.O., Zakaria, O., Zaidan, A.A., Zaidan, B.B., Rame, A.: Video Compression techniques: An Overview. Journal of Applied Sciences 10(16), 183–1840 (2010)Google Scholar
  6. 6.
    Benabdellah, M., Regragui, F., Gharbi, M., Bouyakhf, E.H.: Choice of Rererence Images for Video Compression. Applied Mathematical Sciences 1(44), 2187–2201 (2007)zbMATHGoogle Scholar
  7. 7.
    Carpentieri, B.: Splits, Segs and Superblocks in Split-Merge Video Compression. International Journal of Computers 2(2), 158–164 (2008)Google Scholar
  8. 8.
    Pandit, A.K., Kant, A., Verma, S.: A Prune Modified Algorithm in Video Compression. Ubiqutious Computing and Communication Journal 3(5), 1–11 (2008)Google Scholar
  9. 9.
    Saran, R., Srivastava, H.B., Kumar, A.: Median Predictor-based Lossless Video Compression Algorithm for IR Image Sequences. Defence Science Journal 59(2), 183–188 (2009)CrossRefGoogle Scholar
  10. 10.
    Kilic, I., Yilmaz, R.: A Hybrid Video Compression Based On Zerotree Wavelet Structure. The Arabian Journal for Science and Engineering 34(1B), 187–196 (2009)Google Scholar
  11. 11.
    Bakwad, K.M., Pattnaik, S.S., Sohi, B.S., Devi, S., Lohakare, M.R.: Parallel Bacterial Foraging Optimization for Video Compression. International Journal of Recent Trends in Engineering 1(1), 118–122 (2009)Google Scholar
  12. 12.
    Radhakrishnan, S., Subbarayan, G., Vikram, K.L.: Wavelet Based Video Encoder Using KCDS. The International Arab Journal of Information Technology 6(3), 239–245 (2009)Google Scholar
  13. 13.
    Sankaralingam, E., Thangaraj, V., Vijayamani, S.: Video Compression Using Multiwavelet and Multistage Vector Quantization. The International Arab Journal of Information Technology 6(4), 385–393 (2009)Google Scholar
  14. 14.
    Islam, A., Pearlman, W.A.: An Embedded and Efficient Low-Complexity Hierarchical Image Coder. In: Proc. of the Visual Communications and Image Processing (SPIE), vol. 3653, pp. 294–305 (1999)Google Scholar
  15. 15.
    Koga, T., Linuma, K., Hirano, A., Lijima, J., Ishiguro, T.: Motion Compensated Inter Frame Coding for Video Conferencing. In: Proc. of Natural Telecommunication Conference, New Orleans, pp. G5.3.1–G5.3.5 (1981)Google Scholar
  16. 16.
    Yao, N., Kuang, M.K.: Adaptive rood pattern search for fast block-matching motion estimation. IEEE Transactions on Image Processing 11, 1442–1449 (2002)CrossRefGoogle Scholar
  17. 17.
    Shan, Z., Kuang, M.K.: A new diamond search algorithm for fast block matching motion estimation. IEEE Transactions on Image Processing 9, 287–290 (2000)CrossRefGoogle Scholar
  18. 18.
    Makhoul, J.: Linear prediction: A tutorial review. Proceedings of the IEEE 63, 561–580 (1975)CrossRefGoogle Scholar
  19. 19.
    Luo, Y., Celenk, M.: A new fast block-matching algorithm based on an adaptive rood pattern search using spatio-temporal correlation. In: Proc. of the 10th IASTED International Conference on Signal and Image Processing (SIP 2008), Hawaii (2008)Google Scholar
  20. 20.
    Latte, M.V., Ayachit, N.H., Deshpande, D.K.: Reduced memory listless speck image compression. Digital Signal Processing 16(6), 817–824 (2006)CrossRefGoogle Scholar
  21. 21.
    Brijmohan, Y., Mneney, S.H.: Video Compression for Very Low Bit-Rate Communications Using Fractal and Wavelet Techniques. In: Proc. of the CD-ROM Southern African Telecommunication Networks and Applications Conference (SATNAC), vol. 1, pp. 39–44 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • S. Anantha Padmanabhan
    • 1
  • S. Chandramathi
    • 2
  1. 1.Department of ElectronicsSEA College of EngineeringBangaloreIndia
  2. 2.Department of ElectronicsSRI Krishna College of Engineering and TechnologyCoimbatoreIndia

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