Journal of Electrical Engineering and Technology

  • 제11권2호
  • /
  • Pages.519-528
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  • 2016
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  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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Fast Quadtree Based Normalized Cross Correlation Method for Fractal Video Compression using FFT

  • Chaudhari, R.E. (Dept. of Electronic Engineering (center for VLSI and Nanotechnology), Visvesvaraya National Institute of Technology) ;
  • Dhok, S.B. (Dept. of Electronic Engineering (center for VLSI and Nano-technology), Visvesvaraya National Institute of Technology)
  • 투고 : 2014.12.18
  • 심사 : 2015.10.20
  • 발행 : 2016.03.01
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초록

In order to achieve fast computational speed with good visual quality of output video, we propose a frequency domain based new fractal video compression scheme. Normalized cross correlation is used to find the structural self similar domain block for the input range block. To increase the searching speed, cross correlation is implemented in the frequency domain using FFT with one computational operation for all the domain blocks instead of individual block wise calculations. The encoding time is further minimized by applying rotation and reflection DFT properties to the IFFT of zero padded range blocks. The energy of overlap small size domain blocks is pre-computed for the entire reference frame and retaining the energies of the overlapped search window portion of previous adjacent block. Quadtree decompositions are obtained by using domain block motion compensated prediction error as a threshold to control the further partitions of the block. It provides a better level of adaption to the scene contents than fixed block size approach. The result shows that, on average, the proposed method can raise the encoding speed by 48.8 % and 90 % higher than NHEXS and CPM/NCIM algorithms respectively. The compression ratio and PSNR of the proposed method is increased by 15.41 and 0.89 dB higher than that of NHEXS on average. For low bit rate videos, the proposed algorithm achieve the high compression ratio above 120 with more than 31 dB PSNR.

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참고문헌

  1. A. E. Jacquin, “Image Coding Based on a Fractal Theory of Iterated Contractive Image Transformations,” IEEE Transactions on Image Processing, vol. 1, No. 1, pp. 18-30, 1992. https://doi.org/10.1109/83.128028
  2. Y. Fisher, Fractal Image Compression: Theory and Application, Ed. Springer Verlag, New York, 1995.
  3. M. S. Lazar and L. T. Bruton, “Fractal Block Coding of Digital Video,” IEEE Transactions on Circuits & Systems for Video Technology, vol. 4, No. 3, pp. 297-308, 1994. https://doi.org/10.1109/76.305874
  4. Y. L. Lin and M. S. Wu, “An Edge Property-Based Neighborhood Region Search Strategy for Fractal Image Compression,” Computers & Mathematics with Applications, vol. 62, No. 1, pp. 310-318, 2011. https://doi.org/10.1016/j.camwa.2011.05.011
  5. C. Urmson and K. Ferens, “Video Compression through Fractal Block Coding,” IEEE Canadian Conference on Electrical and Computer Engineering, Vol. 2, pp. 465-467, 1998.
  6. K. U. Barthel and T. Voye, “Three-Dimensional Fractal Video Coding,” IEEE International Conference on Image Processing, vol. 3, pp. 260-263, 1995.
  7. V. D. Lima, R. S. William and H. Pedrini, “Fast Low Bit-Rate 3D Searchless Fractal Video Coding,” IEEE 24th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 189-196, 2011
  8. Y. Fisher, D. Rogovin and T. P. Shen, “Fractal (self-VQ) Encoding of Video Sequences,” In the Proc. of SPIE Visual Communications and Image Processing, vol. 2308, pp. 1359-1370, 1994.
  9. C. S. Kim and S. U. Lee, "Fractal Coding of Video Sequence by Circular Prediction Mapping," In Fractals, vol. 5, pp. 75-88. 1997.
  10. M. Wang and C. H. Lai, “A Hybrid Fractal Video Compression Method,” Computers and Mathematics with Applications, vol. 50, No. 3-4, pp. 611-621, 2005. https://doi.org/10.1016/j.camwa.2004.11.019
  11. M. Wang, R. Liu and C. H. Lai, “Adaptive Partition and Hybrid Method in Fractal Video Compression,” Computers and mathematics with Applications, vol. 51, No. 11, pp. 1715-1726, 2006. https://doi.org/10.1016/j.camwa.2006.05.009
  12. Z. Yao and R. Wilson, “Hybrid 3D Fractal Coding with Neighborhood Vector Quantization,” EURASIP Journal on Advances in Signal Processing, vol. 16, pp. 2571-2579, 2004.
  13. B. Hurtgen and P. Buttgen, “Fractal Approach to Low Rate Video Coding,” in Proc. of the SPIE: Visual Communications and Image Processing ’93, vol. 2094, pp. 120-131, 1993.
  14. C. S. Kim, C. Kim, and S. U. Lee, “Fractal Coding of Video Sequence using Circular Prediction Mapping and Noncontractive Interframe Mapping,” IEEE Transactions on Image Processing, vol. 7, No. 4, pp. 601-605, 1998. https://doi.org/10.1109/83.663508
  15. S. Zhu, L. Li and Z. Wang “A Novel Fractal Monocular and Stereo Video Codec with Object-based Functionality,” EURASIP Journal on Advances in Signal Processing, vol. 2012:227, pp. 1-12, 2012. https://doi.org/10.1186/1687-6180-2012-1
  16. Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, vol. 13, No. 4, pp. 600-612, 2004. https://doi.org/10.1109/TIP.2003.819861
  17. H. Hartenstein and D. Saupe, “Lossless Acceleration of Fractal Image Coding via the Fast Fourier Transform,” Signal Processing: Image Communication, Vol. 16, No. 4, pp. 383-394, 2000. https://doi.org/10.1016/S0923-5965(00)00003-5
  18. S. B. Dhok, R. B. Deshmukh and A. G. Keskar, “Efficient Fractal Image Coding using Fast Fourier Transform,” International Journal on Computing, vol. 1, No. 2, 2011.
  19. K. Belloulata, S. Zhu and Z. Wang, “A Fast Fractal Video Coding Algorithm Using Cross - Hexagon Search for Block Motion Estimation,” International Scholarly Research Network (ISRN) Signal Processing, pp. 1-10, 2011.
  20. S. Zhu, Y. Hou, Z. Wang and K. Belloulata, “Fractal Video Sequences Coding with Region-Based Functionality,” Applied Mathematical Modeling, vol. 36, No. 11, pp. 5633-5641, 2012. https://doi.org/10.1016/j.apm.2012.01.025
  21. K. Belloulata, A. Belalia, S. Zhu, “Object-Based Stereo Video Compression using Fractals and Shape-Adaptive DCT,” International Journal of Electronics and Communications, Vol. 68, No. 7, pp. 687-697, 2014. https://doi.org/10.1016/j.aeue.2014.02.011
  22. J. Wan, L. You, “A Fast Context-Based Fractal Mobile Video Compression with GA and PSO,” 2nd International Conference on Teaching and Computa-tional Science, pp. 112-115, 2014.
  23. C. C. Wang and C. H. Hsieh, “Efficient Fractal Video Coding Algorithm using Intercube correlation Search,” Optical Engineering, vol. 8, No. 39, pp. 2058-2064, 2000.
  24. Y. Bnjmohan and S. H. Mneney, “Low Bit-rate Video Coding Using Fractal Compression of Wavelet Subtrees,” IEEE 7th AFRICON Conference, vol. 1, pp. 39-44, 2004.
  25. Y. Zhang, L. M. Po and Y. L. Yu, “Wavelet Transform Based Variable Tree Size Fractal Video Coding,” IEEE International Conference on Image Processing, vol. 2, pp. 294-297, 1997.
  26. S. D. Wei and S. H. Lai, “Fast Normalized Cross Correlation Based on Adaptive Multilevel Winner Update,” Proc. of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing, pp. 413-416, 2007.
  27. R. E. Chaudhari and S. B. Dhok, “Acceleration of Fractal Video Compression using FFT,” 15th International Conference on Advanced Computing Technologies (ICACT), pp. 1-4, 2013.
  28. B. C. Song, “A Fast Normalized Cross-Correlation Based Block Matching Algorithm Using Multilevel Cauchy-Schwartz Inequality,” ETRI Journal, vol. 33, No. 3, pp. 401-406, 2011. https://doi.org/10.4218/etrij.11.0110.0315
  29. A. A. Eskinder, R. E. Chaudhari, and S. B. Dhok, “Fast Motion Estimation for Quad-Tree Based Video Coder Using Normalized Cross-Correlation Measure,” International Journal of Image processing (IJIP), vol. 7, No. 4, 2013.
  30. Y.M. Zhou, C. Zhang and Z.K. Zhang, “An Efficient Fractal Image Coding Algorithm using Unified Feature and DCT,” Chaos, Solitons & Fractals, vol. 39, No. 4, pp. 1823-1830, 2009. https://doi.org/10.1016/j.chaos.2007.06.089
  31. G. J. Sullivan and R. L. Baker, “Efficient Quadtree Coding of Image and Video,” IEEE Transactions on Image processing, vol. 3, No. 3, pp. 327-331, 1994. https://doi.org/10.1109/83.287030
  32. A. K. Jain, Fundamentals of Digital Image Processing, PHI publications, 1989.
  33. CIPR Sequences, http://www.cipr.rpi.edu/resource/sequences/