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Empirical Evaluation of Decomposition Strategy for Wavelet Video Compression
Rohmad Fakeh, Abdul Azim Abd Ghani
Pages - 31 - 54     |    Revised - 20-02-2009     |    Published - 15-03-2009
Volume - 3   Issue - 1    |    Publication Date - February 2009  Table of Contents
Wavelet Analysis, Decomposition Strategies, Empirical Evaluation
Abstract The wavelet transform has become the most interesting new algorithm for video compression. Yet there are many parameters within a wavelet analysis and synthesis which govern the quality of a decoded video. In this paper different wavelet decomposition strategies and their implications for the decoded video are discussed. A pool of color video sequences has been wavelet-transformed at different settings of the wavelet filter bank and quantization threshold and with decomposition of dyadic and packet wavelet transformation strategies. The empirical evaluation of the decomposition strategy is based on three benchmarks: a first judgment regards the perceived quality of the decoded video. The compression rate is a second crucial factor, and finally the best parameter setting with regards to the Peak Signal to Noise Ratio (PSNR). The investigation proposes dyadic decomposition as the chosen decomposition strategy.
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1 Lin, Y. W., Li, G. L., Chen, M. J., Yeh, C. H., & Huang, S. F. (2010). Repeat-Frame Selection Algorithm for Frame Rate Video Transcoding. International Journal of Image Processing (IJIP), 3(6), 341.
2 Wang, P. C. (2009). Performance Improvement of Vector Quantization with Bit-parallelism Hardware. International Journal of Image Processing (IJIP), 3(4), 152.
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Adami, N., Michele, B., Leonardi, R., and Signoroni, A. “A fully scalable wavelet video coding scheme with homologous inter-scale prediction”. ST Journal of Research, 3(2):19-35, 2006
Adelson, E. H., and Simoncelli, E. “Orthogonal pyramid transforms for image coding”. In Proceedings of SPIE Visual Communications and Image Processing II, 845:50-58, 1987
Albanesi, M. G., Lotto, I., and Carrioli, L. “Image compression by the wavelet decomposition”. European Transactions on Telecommunication, 3(3):265-274, 1992.
Antonini, M., Barlaud, M., Mathieu, P., and Daubechies, I. “Image coding using wavelet transform”. IEEE Transactions on Image Processing, 1(2):205-220, 1992
Antonio, N. “Advances in Video Coding for hand-held device implementation in networked electronic media”. Journal of Real-Time Image Processing, 1:9-23, 2006
Ashourian, M.; Yusof, Z.M.; Salleh, S.H.S.; Bakar, S.A.R.A. “Robust 3-D subband video coder”. Sixth International,Symposium on Signal Processing and its Applications, Vol 2:549– 552, 2001
Brislawn, M. “Classification of non-expansive symmetric extension transforms for multirate filter banks”. Applied and Computational Harmonic Analysis, 3(4): 337-357,1996.
Claudia, S. “Decomposition strategies for wavelet-based image coding”. IEEE International Symposium on Signal Processing and its Applications (ISSPA), Vol. 2: 529-532, Kuala Lumpur, Malaysia, 2001.
D. Taubman and M. Marcellin, JPEG2000: Image Compression Fundamentals, Standards and Practice, Boston: Kluwer Academic Publisher, 2002.
D. Taubman, “High performance scalable image compression with EBCOT”, IEEE Transaction on Image Processing, Vol. 9, No. 7, pp. 1158-1170, July 2000.
Daubechies, I. and Sweldens, W. “Factoring wavelet transforms into lifting steps”. Journal of Fourier Analysis and Applications, 4(3):245-267, 1998.
Daubechies, I. “Orthonormal bases of compactly supported wavelets". Commun. Pure Applied. Math, 41:909-996, 1988.
Daubechies, I. “Ten Lectures on Wavelets”. Philadelphia, Pennsylvania: Society for Industrial and Applied Mathematics (SIAM), 1992
Daubechies, I. “The wavelet transform, time-frequency localization and signal analysis”. IEEE Trans. on Information Theory, 36(5):961-1005, 1990.
Donoho, L. and Johnstone, I. M. “Ideal spatial adaptation via wavelet shrinkage”. Biometrika, 81(3):425-455, 1994.
Donoho, L. and Johnstone, I. M. “Minimax estimation via wavelet shrinkage”. The Annals of Statistics, 26(3):879-921, 1998.
Donoho, L. “De-noising by soft-thresholding”. IEEE Transactions on Information Theory, 41(3):613-627, 1995.
George, F., Dasen, M., Weiler, N., Plattner, B., and Stiller, B. “The wavevideo system and network architecture: design and implementation”. Technical report No. 44. Computer Engineering & Networks Laboratory (TIK), E7H, Zurich, Switzerland, 1998.
Golwelkar, A. V. and Woods, J. W. “Scalable video compression using longer motioncompensated temporal filters”. VCIP 2003: 1406-1416, 2003
Hsiang, S. T. and Woods, J. W. “Embedded video coding using invertible motion compensated 3-D subband/wavelet filter bank”. Journal of Signal Processing: Image Communication, Vol. 16: 705-724, 2001.
Icon Image (http://graphics.cs.brown.edu/games/G3D/icon.jpg).
JPEG2000 Part1: Core Coding System, Final Committee Draft (ISO/IEC FCD 15444-1), ISO/IEC JTC1/SC29/WG1 N11855, March 2000.
JPEG2000 Part2: JPEG2000 Extension, Final Committee Draft (ISO/IEC FCD 15444- 2),November 2001.
Karlsson, G. and Vetterli, M. “Three dimensional subband coding of video”.In Proceedings of IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), II:1100-1103, 1988.
Lewis, A. S. and Knowles, G. “Image compression using the 2-D wavelet transform”. IEEE Trans. Image Processing, 1:244-250, 1992.
Lewis, A. S. and Knowles, G. “Video compression using 3D wavelet transforms”. Electronic Letters, 26(6):396-398, 1990.
Luo, J. “Low bit rater wavelet-based image and video compression with adaptive quantization, coding and post processing”. Technical Report EE-95-21. The University of Rochester, School of Engineering and Applied Science, Department of Electrical Engineering, Rochester, New York, 1995.
M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, “Image coding using wavelet transform”, IEEE Transaction on Image Processing, Vol. 1, pp. 205-220, April 1992.
Mallat, S. G. 1989. “A theory for multiresolution signal decomposition: the wavelet representation”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674- 693, 1989.
Pencil Image (http://www.stpaulcareers.umn.edu/img/assets/16141/Graphic%20Design145x100.jpg).
Ping Sing Tsai, and Ricardo Suzuki, “Graphics Image Compression Using JPEG2000”, IEEE 2008 Congress on Image and Signal Processing, pp. 603-607, 2008.
Podilchuk, Jayant, N. S., and Farvardin, N. “Three-dimensional subband coding of video”. IEEE Translation of Image Processing, 4(2): 125-139, 1995.
R C Gonzalez, and R.E. Woods, “Digital Image Processing”, 2nd Edition, Pearson Education.
Serene Banerjee and Brian L Evans, “Tuning JPEG2000 Image Compression for Graphics Region”, Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, pp 1- 5, 2002.
Shapiro, J. M. “Embedded image coding using zerotrees of wavelets coefficients”. IEEE Transactions on Signal Processing, 41(12):3445-3462, 1993.
Strang, G., and Nguyen, T. “Wavelets and Filter Banks”. Wellesley-Cambridge Press, Wellesley, MA, USA, 1997.
Taubman, D., and Zakhor, A. “Multirate 3-D subband coding of video”. IEEE Transactions on Image Processing, 3(5): 572-588, 1994
Vass, J., Zhuang, S., Yao, J., and Zhuang, X. “Mobile video communications in wireless environments”. In Proceedings of IEEE Workshop on Multimedia Signal Processing, Copenhagen, Denmark: 45-50, 1999
Wallace, G. K. “The JPEG still picture compression standard”. Comm. ACM, 34(4):30-44, 1994.
Wang, X., and Blostern, S. D. 1995. “Three–dimensional subband video transmission through mobile satellite channels”. In Proceedings of International Conference on Image Processing, Vol. 3, pp. 384-387, 1995.
Mr. Rohmad Fakeh
- Malaysia
Mr. Abdul Azim Abd Ghani
- Malaysia