Quality Verification of Audio and Image Modulation by the Simulation of PCM, DM and DPCM Systems
International Journal of Information and Communication Sciences
Volume 3, Issue 4, December 2018, Pages: 110-120
Received: Feb. 28, 2019;
Accepted: Apr. 4, 2019;
Published: May 8, 2019
Views 276 Downloads 19
Gaby Abou Haidar, Department of Computer and Communications Engineering, American University of Science and Technology, Beirut, Lebanon
Roger Achkar, Department of Computer and Communications Engineering, American University of Science and Technology, Beirut, Lebanon
Hasan Dourgham, Department of Computer and Communications Engineering, American University of Science and Technology, Beirut, Lebanon
Modulation is a process through which a message has to pass in order to be effectively transmitted. However, there are some limitations to Pulse Code Modulation and Delta Modulation that can cause data redundancy, quantization error, slope overload distortion and granular noise which result in a bad communication process. Throughout the past few years, Pulse Code Modulation (PCM), Delta Modulation (DM) and Differential Pulse Code Modulation (DPCM), in digital communication systems, have proven to have unparalleled advantages over analog communication systems; this is in terms of error minimization and distances of transmission enhancements. Delta Modulation, a simplified version of Pulse Coded Modulation also pauses major problems in noise and quantization error. Consequently, and to combat the arising problems, communication engineers have developed newly adaptive compression and modulations techniques for better digital transmission. One of these innovative systems is the Differential Pulse Coded Modulation (DPCM) that can solve the aforementioned problems. Thus the focal point of this article is to explore the simulation of these systems using Simulink (The Math Works, Inc., USA). Eventually, the systems are tested on both image and audio inputs to prove the superiority of DPCM over DM and PCM systems in reducing noise and increasing the signal to quantization noise ratio, thus insuring a smooth and successful transfer of data.
Gaby Abou Haidar,
Quality Verification of Audio and Image Modulation by the Simulation of PCM, DM and DPCM Systems, International Journal of Information and Communication Sciences.
Vol. 3, No. 4,
2018, pp. 110-120.
Simon Haykin. “Communication Systems”, New York: John Wiley and Son, Inc., 2000.
Robert Galloger. “Introduction to Digital Communications”, Internet: http://ocw.mit.edu/courses/electrical-engineering-and-computer science /6-450-principles-of-digital-communications-i-fall-2006/lecture-notes / book_1.pdf, [Jun. 25, 2015].
C. Mansour, R. Achkar, G. Abou Haidar “Simulation of DPCM and ADM Systems”, IEEE 14th International Conference on Modelling and Simulation, UKSim 2012 Cambridge, United Kingdom March 28-30, pp. 416-421.
G. Abou Haidar, R. Achkar and H. Dourgham, “A Comparative Simulation Study of the Real Effect of PCM & DPCM Systems on Audio and Image Modulation” IEEE International Multidisciplinary Conference on Engineering Technology (IMCET 2016), Beirut, Lebanon, 2-4 November 2016, pp 144-149.
Widrow, J. Glover, J. M. McCool, J. Kaunitz, C. S. Williams, H.Hearn, J. R. Zeidler, E.Dong, and R. Goodlin,“Adaptive noise cancelling: Principles and applications ”, Proc. IEEE, vol. 63, pp.1692-1716, Dec. 1975.
William N. Waggener (1999). “Pulse Code Modulation Systems
Design”, 1st ed., Boston, MA: Artech House.
B.M Oliver, J.R Pirece, and C.E Shannon. “The Philosophy of PCM”. Proceeding of the IRE 36.
N. S. Jayant and A. E. Rosenberg. "The Preference of Slope Overload to Granularity in the Delta Modulation of Speech". The Bell System Technical Journal, Volume 50, no. 10, December 1971.
C.E Shannon, “Communications in the presence of noise”, Proc.
Institute of Radio Engineers, vol. 37, no. 1 pp 10-21.
Dony, R. D., and Haykin, S., (1995), Neural Network Approaches to Image Compression, Proceedings of the IEEE, Vol. 23, No. 2, pp 289-303.
Egger O, Fleury P, Ebrahimi T, Kunt M (1999) High-Performance Compression of Visual Information-A Tutorial Review-Part I: Still Pictures. In: Proceedings of the IEEE, vol. 87, no 6, June 1999.
M. J. Weinberger, G. Seroussi and G. Sapiro, The LOCO-I Lossless Image Compression Algorithm: Principles and Sta- ndardization into JPEG-LS, IEEE Transaction on Image Processing, Vol. 9, No. 8, 2000, pp. 1309-1324.
G. W. Cottrell and P. Munro, “Principal components analysis of images via back propagation,” in SPIE Vol. 1001 Visual Communications and Image Processing ’88, 1988, pp. 1070–1077.
Nelson, M., (1991), The Data Compression Book, M & T Publishing Inc.
T. Acharya and A. K. Ray, Image Processing: Principles and Applications. Hoboken, NJ: John Wiley & Sons, 2005.
Chafic Saide, R´egis Lengelle, Paul Honeine, C´edric Richard, and Roger Achkar. Nonlinear adaptive filtering using kernel-based algorithms with dictionary adaptation. International Journal of Adaptive Control and Signal Processing, 29(11):1391–1410, 2015.
S Jayaraman, S Esakkirajan, T Veerakumar, “Digital Image Processing”, Tata Mc Graw Hill Educaation Private Limited, 2009.
Noll P (1997) MPEG digital audio coding. In: IEEE Signal Processing Magazine vol 14, no 5, pp. 59-81, Sept 1997.
Scott Umbaugh, “Computer Vision and Image Processing”, Prentice Hal Intl., Inc., 1988.
R. C. Gonzalez, R. E. Woods and S. L. Eddins, Digital Image Processing Using MATLAB, (Pearson Edition, Dorling Kindersley, London, 2003).