Investigation of the Performance of Modified TCM Scheme for the Protection of SPIHT-Based Compressed Images Over Fading Channel
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In this paper, a new variant of trellis-coded modulation with Ungerboeck-Gray mapping 'TCM-UGM' is presented for spectral efficiency of 2 bit/s/Hz. The performance of this encoding scheme is investigated over Rayleigh fading channel. The simulation result, using 16-state TCM-UGM encoder, shows clearly that the proposed scheme outperforms the performance of the 32-state TCM by 2.8 dB at BER=10-5. Both compression and transmission errors may degrade the quality of images. To illustrate the effectiveness of the proposed system for transmission the compressed image with the CDF9/7 wavelet and SPIHT coding, a comparison between the TCM and the new variant the TCM-UGM is presented. Simulation results on several types of compressed image were presented. For this study, four Image Quality Metrics (IQMs) were employed in the image quality assessment after the transmission. Results show that the new variant of TCM-UGM reduces transmission errors and better protects the compressed image during transmission.
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G. Ungerboeck, “Channel coding with multilevel/phase signals,” IEEE Transactions on Information Theory, vol. 28, pp. 55–67, January 1982.
G. D. Forney, Jr., The Viterbi Algorithm, Proc. IEEE, vol.61, Mar. 1973, pp. 268-278.
A. Bassou and A. Djebbari, “Contribution to the Improvement of the Performance of Trellis-Coded Modulation,” WSEAS Transactions on Communications, Vol. 6, No. 2, 2007, pp. 307-311.
V. Chappelier, Progressive Coding of Images by Directed Wavelet, Phd Thesis, Rennes 1 University, 2005.
S. Mallat, “Multifrequency Channel Decompositions of Images and Wavelet Models,” IEEE Transaction in Acoustic, Speech and Signal Processing, Vol. 37, No. 12, Dec. 1989, pp. 2091-2110.
D. Samai, N. Doghmane, M. Bedda, “Comparative Performance of Embedded Coders at High Quality,” International review of on Computers and Software (IRECOS), Vol. 3. N. 6, pp. 639-644, November 2008
J.M. Shapiro, “Embedded Image Coding using Zerotrees of Wavelet Coefficients,” IEEE Trans. Signal Processing, Vol. 41, No 12, Dec. 1993, pp. 3445 – 3462.
A. Said, W.A. Pearlman, “A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,” IEEE Trans. Circuits Syst. Video Technol., Vol. 6, No. 3, June 1996, pp. 243-250
Antonini, M., Barlaud, M., Mathieu, P., and Daubechies, I., “Image Coding Using Wavelet Transform,” IEEE Transactions Image Process, Vol. 1, No. 2, pp. 205-220, 1992.
J.D. Villasenor, B. Belzer, J. Liao, “Wavelet Filter Evaluation for Image Compression,” IEEE Transactions on Image Processing, Vol. 4, No. 8, Aug. 1995, pp.1053-1060.
D.S. Taubman, M.W. Marcellin, JPEG2000: Image Compression Fundamentals, Standards and Practice, Kluwer Academic Publishers, London, 2002.
S.S. Gornale, R.R. Manza, Vikas Humbe and K.V. Kale, “Performance Analysis of Biorthogonal Wavelet Filters for Lossy Fingerprint Image Compression,” International Journal of Imaging Science and Engineering, Vol. 1, No.1, pp. 16-20, January 2007.
H.R. Sheikh, M.F. Sabir, and A.C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms,” IEEE Trans. Image Processing, 15(11), pp. 3440-3451, Nov. 2006.
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