Algorithm for Multimedia Compression

International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611 Algorithm for Multimedia Compression Sara Mohammed Salih Khater1, Ashraf GasimElsid2, Amin Babiker A/Nabi3 1 Department of Data and Communication Network, Al Neelin University/ Faculty of Engineering, Khartoum, Sudan 2 Department of Electronics Engineering, Collage of Engineering, Sudan University and Faculty of Telecommunication and Space Technology, Future University, Khartoum, Sudan 3 Department of Communication Engineering, Al Neelin University/ Faculty of Engineering, Khartoum, Sudan. Abstract: In this paper multimedia compression is proposed for multimedia application to fit the available bandwidth. This leads to reduction of the bandwidth problem in multimedia network. This compression algorithm is efficient When the traffic load is high in this study a 45 E and 90E load are used. The algorithm was modeled using MATLAB program .The simulation model was build based on a mathematical model .The simulation result shows a good performance of the algorithm during high traffic load. Keyword: bandwidth, multimedia, traffic load 1. Introduction capable of encoding and decoding data which consists of an Encoder and a Decoder, transcoding is a conversion from Multimedia refers to content that uses a combination of one encoded digital representation into another one. A different content forms. This contrasts with media that use fundamental term in the area is compression rate (or only rudimentary computer displays such as text-only or compression ratio) which denotes the relation between the traditional forms of printed or hand-produced material. size of the original data before compression and the size of Multimedia includes a combination of text, audio, still the compressed data. Compression ratio therefore rates the images, animation, video, or interactive content forms. effectivity of a compression system in terms of data reduction capability. This must not be confused with other Multimedia can be recorded and played, displayed, dynamic, measures of compressed data size like bit per pixel (bpp) or interacted with or accessed by information content processing bit per sample (bps). devices, such as computerized and electronic devices, but can also be part of a live performance. Multimedia devices are During the last years three important trends have contributed electronic media devices used to store and experience to the fact that nowadays compression technology is as multimedia content. Multimedia is distinguished from mixed important as it has never been before – this development has media in fine art; by including audio, for example, it has a already changed the way we work with multimedial data like broader scope. The term "rich media" is synonymous for text, speech, audio, images, and video which will lead to interactive multimedia. Hypermedia scales up the amount of new products and applications: media content in multimedia application.  The availability of highly effective methods for Since media is the plural of medium, the term "multimedia" is compressing various types of data. used to describe multiple occurrences of only one form of  The availability of fast and cheap hardware components to media such as a collection of audio CDs. This is why it's conduct compression on single-chip systems, important that the word "multimedia" is used exclusively to microprocessors, DSPs and VLSI systems. describe multiple forms of media and content. Multimedia  Convergence of computer, communication, consumer involves multiple modalities of text, audio, images, drawings, electronics, publishing, and entertainment industries. animation, and video. Compression is enabled by statistical and other properties of 2. Multimedia Compression most data types, however, data types exist When talking abount compression, we often mean “lossy a) Classification of Techniques: compression” while “lossless compression” is often termed as  Lossless: recover the original representation coding. However, not all coding algorithm do actually lead to  Lossy: recover a representation similar to the original lossless compression, e.g. error correction codes. Like in one every other field in computer science or engineering, the o high compression ratios dominating language in compression technologies is English o more practical use of course. There are hardly any comprehensive and up-todate  Hybrid: JPEG, MPEG, px64 combine several German books available, and there do NOT exist any German approaches journals in the field. Codec denotes a complete system Volume 4 Issue 10, October 2015 www.ijsr.net Paper ID: SUB158262 1012 Licensed Under Creative Commons Attribution CC BY  International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611 Table 1: Multimedia compression Short Name Official Name Standards Group Compression Ratios JPEG Digital compression and coding of continuous-tone Joint photographic Experts 15:1 still images Group Full color still-frame applications H.261 or Px64 Video encoder/ decoder for audio-visual services Specialist Group on coding 100:1 to at px64 Kbps for visual telephony 2000:1 Video –based tele- communications MPEG Coding of moving pictures an associated audio Moving pictures Experts 200:1 Group Motion-intensive applications 3. Multimedia Compression Efficiency c) Lossless Image Compression  Approaches of Differential Coding of Images: The compression efficiency indicate the strength of the  Given an original image I(x, y), using a simple compression algorithm and it can be computed using equation difference operator we can define a difference image (1). In multimedia it can be represented by the cumulative d(x, y) as follows: sum of the various media. 𝑑 𝑥, 𝑦 = 𝐼 𝑥, 𝑦 − 𝐼(𝑥 − 1, 𝑦) 𝑓𝑖𝑙𝑒 JPEG d) Lossless 𝑠𝑖𝑧𝑒 𝑏𝑒𝑓𝑜𝑟𝑒 − 𝑓𝑖𝑙𝑒 𝑠𝑖𝑧𝑒 𝑎𝑓𝑡𝑒𝑟 𝑐𝑒Predictive The = method: ∗ 100% 𝑓𝑖𝑙𝑒 𝑠𝑖𝑧𝑒 𝑏𝑒𝑓𝑜𝑟  Forming a differential prediction: A predictor 4. Multimedia compression Algorithm combines the values of up to three neighboring pixels as the predicted value for the current pixel, indicated Compression: the process of coding that will effectively by `X' in Figure. The predictor can use any one of the reduce the total number of bits needed to represent certain seven schemes listed in the below Table. information.  Encoding: The encoder compares the prediction with the actual pixel value at the position `X' and encodes the difference using one of the lossless compression techniques, e.g., the Human coding scheme 2) Lossy Compression Figure 1: General Data Compression Scheme  digital audio, image, video where some errors or loss can be tolerated 1) Classification  exploit both data redundancy and human perception properties a) Lossless Compression  Compressed data is not the same as the original data,  lossless compression for legal and medical documents, but a close approximation of it. computer programs  Yields a much higher compression ratio than that of  exploit only data redundancy lossless compression.  Variable-Length Coding (VLC): the more frequently- appearing symbols are coded with fewer bits per 3) Distortion Measures : symbol, and vice versa.  mean square error (MSE)  2 . 𝜎2 = 1 𝑁 𝑛=1 (𝑥𝑛 − 𝑁  Shannon-Fano Algorithm : 𝑦𝑛)2 o Sort symbols according to the frequency of where xn, yn, and N are the input data sequence, occurrences. reconstructed data sequence, and length of the data o Recursively divide symbols into two parts, each with sequence respectively. approximately same counts, until all parts contain 𝜎 2𝑥 only one symbol.  signal to noise ratio (SNR). 𝑆𝑁𝑅 = 10 log 10 𝜎 2𝑑 b) Huffman Coding 𝜎 2 𝑥 is the average square value of the original data  Initialization: Put all symbols on a list sorted according sequence and 𝜎 2 𝑑 is the MSE. to frequency.  peak signal to noise ratio (PSNR) .𝑃𝑆𝑁𝑅 =  Repeat until the list has only one symbol left: 𝜎 2 𝑝𝑒𝑎𝑘 o From the list pick two symbols with the lowest 10 log 10 𝜎 2𝑑 frequency counts. Form a Human subtree that has Which measures the size of the error relative to the these two symbols as child nodes and create a parent peak value of the signal Xpeak node. o Assign the sum of the children's frequency counts to the parent and insert it into the list such that the order 5. Simulation Scenario is maintained. Figure 1 shows the block diagram for the simulation o Delete the children from the list. program. This program is written using MATLAB  Assign a codeword for each leaf based on the path from instruction. the root.  statistics are gathered and updated dynamically as data increments the frequency counts for the symbols. Volume 4 Issue 10, October 2015 www.ijsr.net Paper ID: SUB158262 1013 Licensed Under Creative Commons Attribution CC BY  International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611 Figure 4 shows the histogram where X axis is the interarrival time [s] and Y axis represented frequency . Figure 4: Histogram of interarrival Figure 5 shows the The distribution of call arrival time where X axis is User ID and Y axis represented arrival time [s] 6. Result and Discussion Figure 3 shows that simulation windows and it represented the random distribution of user inside the building as defined previously, the X axis represent window length and Y axis represented width equal 4km both of them. The distribution of the mobile in the simulation area 2000 Figure 5: The distributon of call arrival time 1500 Figure 6 shows the The required bandwidth where X axis is 1000 User ID and Y axis represented Bandwidth[Kbps]. Table (1) show the compression efficiency. 500 0 -500 -1000 -1500 -2000 -2000 -1500 -1000 -500 0 500 1000 1500 2000 Figure 3: simulation model window Volume 4 Issue 10, October 2015 www.ijsr.net Paper ID: SUB158262 1014 Licensed Under Creative Commons Attribution CC BY  International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611 [3] “Security for real-time MPEG compressed video in distributed multimedia applications,” in Proc. 15th IEEE Int. Phoenix Conf. Comput. Commun., Mar. 1996. [4] H. Cheng and X. Li, “Partial encryption of compressed images and videos,” IEEE Trans. Signal Process., vol. 48, no. 8, p. 2439, Aug. 2000. [5] C.-P. Wu and C.-C. J. Kuo, “Fast encryption methods for audio-visual data confidentiality,” in SPIE Int. Symp. Information Technologies 2000, vol. 4209, Nov. 2000, p. 284. [6] A. Barbir, “A methodology for performing secure data compression,” in Proc. 29th Southeastern Symp. System Theory, Mar. 1997, p. 266. [7] H. Bergen and J. Hogan, “A chosen plaintext attack on an adaptive arithmetic coding compression algorithm,” Comput. Secur., vol. 12, p. 157,1993. [8] D. Gillman, M. Mohtashemi, and R. Rivest, “On breaking a Huffman code,” IEEE Trans. Inform. Figure 6: The requried BW Theory, vol. 42, no. 3, p. 972, May 1996. [9] W. B. Pennebaker and J. L. Mitchell, JPEG Still Image Table 1: The Compression Efficiency Data Compression Standard. New York: Van Nostrand CE 45 E 90 E Reinhold, 1993. 0 1 16 [10] Fox, E.A (1991) Adanced in interactive digital 10 5 12 multimedia systems. IEEE computer , 24 : 9-21 . 40 0 5 [11] Furht, B. (1994) multimedia systems: an overview. 80 0 0 IEEE multimedia, 1:47-59. [12] Ao.Univ.-Prof. Dr. Andreas Uhl , Compression Technologies and Multimedia Data Formats . Author Profile Sara Mohammed Salih Khater, received the B.Sc. degree in computer engineering from Al gzira University in 2010. She is currently pursuing the M.SC degree with the Department of Data and Communication Network, Al neelain University, Khartoum, Sudan. Her research interests include Mobile system, Data and Communication Networking. Ashraf Gasim Elsid Abdalla, Associate professor in telecommunication Engineering and researcher in space technology center in future university. Also he is academic members of electronic department in college Figuer 7: The compression efficiency of engineering, Sudan University of science and technology. He was a former lecturer and researcher in many Malaysia Universities; UKM, UPM, UIA and MMU. He got his 7. Conclusion Ph.D. and M.Sc. from National university of Malaysia 2001 and 1996 in electrical and electronic system. He got his B.Sc. in The results indicate reduction in the connection failure due to electronic engineering from technical university of Budapest 1993. the increase in the compression efficiency. The amount of the His research focus on Mobile and satellite communication. He reduction can be observed during the high traffic load. The published more than 40 technical papers and supervised more than amount of the connection loss is higher in the heavy traffic 50 Ph.D. and Master Students. load. Amin Babiker A/Nabi Mustafa, obtained his B.Sc. and M.Sc. from the University of Khartoum in 1990 References and 2001, respectively. He obtained his Ph. D. from Al neelain University in 2007. He was the Head of the [1] C.-P.Wu and C.-C. J. Kuo, “Efficient multimedia Dept. of Computer Engineering from 2001 to 2004. encryption via entropy codec design,” in Proc. SPIE Int. Then, he became the Vice Dean. He has been the dean since 2009 Symp. Electronic Imaging 2001, vol. 4314, Jan. 2001, p. until 2014 then he is currently the Vice consoler for academic 128. affairs. His research areas include QoS in telecommunications, [2] W. Zeng and S. Lei, “Efficient frequency domain Traffic Engineering and Service Costing Disciplines. Associate selective scrambling of digital video,” IEEE Trans. prof. Dr. Amin is a Consultant Engineer. He is a member of the Sudan Engineering Council. Multimedia, vol. 5, no. 1, p. 118, Mar. 2003. Volume 4 Issue 10, October 2015 www.ijsr.net Paper ID: SUB158262 1015 Licensed Under Creative Commons Attribution CC BY