INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014
UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: firstname.lastname@example.org Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING Vol.4 No.8 August 2014
From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. The conference was held with the Association for Feminist, Epistemologies, Methodologies, Metaphysics, and Science Studies (FEMMSS) and the Canadian Society for Women in Philosophy (CSWIP) on August 10 to 13, 2014 on Science, Technology, and Gender Challenges and Opportunities. Stimulating nerves in your ear could improve the health of your heart, researchers have discovered. A team at the University of Leeds used a standard TENS (Transcutaneous electrical nerve stimulation) machine like those designed to relieve labour pains to apply electrical pulses to the tragus, the small raised flap at the front of the ear immediately in front of the ear canal. The stimulation changed the influence of the nervous system on the heart by reducing the nervous signals that can drive failing hearts too hard. University of Waterloo researchers Saini got the idea for the sensors from butterfly wings. â€œI have always been fascinated by these beautiful nanostructures,â€? he says. Once, while viewing a butterfly wing under a microscope, he accidentally dropped some water on it and the color changed. This got him thinking about whether he could generate colors in chips. Currently, it takes days to process water samples and other optical chips being researched require expensive equipment. This new method is quick and inexpensive and uses the connectivity of cell phones to rapidly warn people about contaminated water. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.
Thanks, Editorial Team IJITCE
Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.
Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014 Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore. Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014 Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE
Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,S찾o Paulo Business School,RuaItapeva, 474 (8째 andar)01332-000, S찾o Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014 Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677
Contents Video Compression by Using H.264/MPEG-4 AVC by Exploiting Spatial Correlation by Veerendra Nath.D, M.V.Srikanth…………………………………………………………………………………………….……………………….
Video Compression by Using H.264/MPEG-4 AVC by Exploiting Spatial Correlation Veerendra Nath.D M.Tech Student,Department of ECE, Gudlavalleru Engineering College, Gudlavalleru -521356,A.P,INDIA Email: email@example.com M.V.Srikanth Assistant Professor,Department of ECE, Gudlavalleru Engineering College, Gudlavalleru -521356,A.P,INDIA Email: firstname.lastname@example.org Abstract---This paper presents a novel approach to Video Compression in H.264/MPEG-4 Advanced Video Coding (AVC) compressed video streams for Spatial Correlation. We present a new video compression approach which tends to hard exploit the pertinent temporal redundancy in the video frames to improve compression efficiency with minimum processing complexity.There are two new intramodes are developed to better exploit spatial correlation. The first is the residual scalar quantization (RSQ) mode, second is the base colors and index map (BCIM) mode that can be viewed as an adaptive color quantization. Every block selects its coding mode from two new modes and the previous intramodes in H.264 by rate-distortion optimization (RDO).This transformation turns the spatial temporal correlation of the video into high spatial correlation.Indeed, this technique transforms each group of pictures to one picture eventually with high spatial correlation. Thus, the decorrelation of the resulting pictures by the DCT makes efficient energy compaction, and therefore produces a high video compression ratio. Many experimental tests had been conducted to prove the method efficiency especially in high bit rate and with slow motion video. The proposed method seems to be well suitable for video surveillance applications and for embedded video compression systems. Keywords— H.264, AVC, video compression, inter mode decision.
I . INTRODUCTION The objective of video coding in most video applications is to reduce the amount of video data for storing or transmission purposes without affecting the visual quality. The desired video performances depend on applications requirements, in terms of quality, disks capacity and bandwidth. For portable digital video applications, highlyintegrated real-time video compression and decompression solutions are more and more required. Actually, motion estimation based encoders are the most widely used in video compression. Such encoders exploits inter frame correlation to provide more efficient compression. Recent acceptance of H.264 as a new decoding typical is expected to have far more implications than the production of presently a new documentation. The consensus among the foremost players of the communications and video industry on H.264 might afford the major thrust for this new standard. Previous MPEG video coding principles such as MPEG-1 and MPEG-2 have enabled many familiar buyer products. For instance, these standards enabled video CD’s and DVD’s
tolerate video playback on digital VCRs/set-top-boxes and computers. The MPEG-2 video coding standard,which was urbanized about 10 years ago primarily as a conservatory of prior MPEG-1 video capability to bear of interlaced video coding, was an enabling technology for digital television systems worldwide. It is exploit for transmission of standard definition (SD) and high definition (HD) TV signals over satellite, cable and global emission and the storage of high quality SD video signals onto DVDs. MPEG-4 was launched to address a new cohort of multimedia applications and services such as interactive TV, internet video etc. The core of the MPEG-4 usual was developed during 1995-1999,however MPEG-4 is a living usual with new parts added continuously as and when technology exists to address embryonic applications. The significant advances in core video usual were achieved on the capability of coding video objects, even as at the same time, improving coding efficiency at the expense of a reserved increase in complexity. The usual achieves clearly higher compression efficiency, often quoted as, up to a feature of two over the MPEG-2 video standard. As one would expect, the augment in compression efficiency comes at the cost of considerable increase in complexity, often quoted as a factor of four for the decoder, whereas encoding difficulty may be as high as a factor of nine over MPEG-2. Moreover, as of flexible features or subsets of the standard, the ensuing complexity depends on the profile implemented, which is submission dependent. II. Oveview of the H.264 Standard In order to address the need for suppleness and customizability, the H.264 standard covers a Video Coding Layer (VCL), which is designed for capable representation of the video content, and a Network Abstraction Layer (NAL), which formats the VCL illustration of the video and provides header information in a way that is suitable for conveyance by different transport layers or storage media. Figure 1 depicts the configuration of H.264/AVC video encoder. As in all prior ITU-T and ISO-IEC JTC1 video standards, the H.264 VCL design follows the so-called block-based hybrid video coding approach. The basic coding configuration for a macroblock is depicted in Figure 2. There is no single coding constituent which provides the majority of the improvement in density efficiency. It is rather a plurality of smaller improvements that add up to the considerable gain. A coded video sequence in H.264 consists of a succession of coded pictures. A coded picture represents either an entire
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014 frame or a lone field, as was also the case in the MPEG-2 video. H.264 uses 4:2:0 sampling design in which chroma (Cb and Cr) samples are aligned horizontally with every second luma sample and are located vertically among two luma samples. A picture is partitioned into fixed-size macroblocks that each cover a rectangular picture area of 16 x 16 samples of the Luma constituent and 8 x 8 samples of each of the Chroma components. A picture may be split into one or a number of slices. In H.264 slices consist of macroblocks processed in raster scan order when not via flexible macroblock ordering (FMO). Using FMO, a picture can be split into many macroblock scanning outline such as interleaved slices, dispersed macroblock portion, one or more “foreground” slice groups and a “leftover” slice group, or a checkerboard category of mapping.
Figure 1. Structure of H.264/AVC video encoder
Figure 2. Basic coding structure of H.264/AVC for a MB Each slice can be coded by using I, P, B, SP and SI frames. The first three are very comparable to those in previous standards with the exclusion of the use of reference pictures as described in the following. SP and SI slices, which are so-called switching P and I slices correspondingly, are the new ones. SP slices aim at efficient switching among different versions of the same video sequence whereas SI slices aim at arbitrary access and error recovery. All Luma and Chroma samples of a macroblock are either spatially or temporally forecast, and the prediction residual is encoded by an integer transform. The transform coefficients are quantized and encoded using entropy coded methods (Figure 2). There are several new features and possibilities in H.264 for Intra/Inter-frame prediction. The types of intra coding support are denoted as Intra-4 x 4 or Intra- 16 x 16, which are lame forecast models, together with a chrome prediction (8 x 8) and I-PCM (i.e. Direct) forecast modes. The Intra-4 x 4 mode is well suited for coding of picture parts with considerable detail while the Intra-16 x 16 mode is more suitable for very smooth areas of the picture. The I-PCM
mode allows the encoder to purely bypass the prediction and transform coding processes and express sending of the values of the encoded samples. Each of the 4 x 4 luma blocks can be predicted using moreover the dc mode or one of the eight coding directions scheduled in Figure 3(c) and illustrated in Figure 3(a). For the use of illustration, Figure 3 (b) shows a 4 x 4 block of pixels a, b, c, .., P, belonging to a macroblock to be coded . Pixels A, B, C, .., H and J, K, L, M are previously decoded neighboring pixels used in computation of forecast of pixels of current 4 x 4 block. Directional predictions use a linear weighted average of pixels of A through H and I through M, depending on the specific direction of the forecast. When utilizing the Intra-16 x 16 mode, four prediction modes are supported. Forecast mode 0 (vertical prediction), mode 1 (horizontal prediction), mode 2 (DC prediction), and mode 3 (plane prediction) are particularly similar to the modes in Intra-4 x 4 prediction except the number of neighboring pixels. The 8 x 8 chroma mode also uses a prediction procedure which is similar to the one for Intra-16 x 16. H.264 standard is more flexible in the selection of motion compensation (MC) block sizes and shapes than any previous usual, with a minimum Luma MC block size as small as 4 x 4. Figure 4 illustrates the macroblock partitioning for MC prediction. The precision of MC is in units of one quarter of the distance between Luma samples. Predicted values at halfsample positions are obtained by applying a one dimensional 6-tap FIR filter horizontally and vertically. Predicted values at quarter-sample positions are produced by averaging samples at integer and half-sample positions. Since it is 4:2:0 video format, the displacements used for Chroma have one-eight sample location accuracy. The motion vector components are differentially coded using moreover median or directional prediction from neighboring blocks. The H.264 syntax supports multi-picture motioncompensated prediction , in which extra than one previously coded picture can be used as a position for MC prediction. This new feature requires both encoder and decoder to store the position pictures used for inter prediction in a multipicture buffer. Multiple position pictures not only contribute to the improvement of the compression efficiency, but also help error revival. In addition to the motion-compensated macroblock modes, a P macroblock can also be coded in PSkip type. With this coding, neither quantized forecast error signal, nor a motion vector is transmitted. The useful effect of P-Skip mode is that large areas with no change or steady motion like slow panning can be represented with very few bits.
Figure 3. a) Intra-4* 4 prediction directions, b) block prediction process, c) prediction modes
Figure 4. Macroblock partitions, sub-macroblock partitions and partition scans The concept of B slices is sweeping in H.264 when compared with prior video coding standards . In B slices, to build the forecast signal, some macroblocks or blocks may use a weighted standard of two distinct motion-compensated prediction values. B slices employ two distinct lists of position pictures, which are referred to as the first (list 0) and the second (list 1) reference picture lists. Four dissimilar types of inter prediction are supported: list 0, list 1, bipredictive, and straight prediction. For the bi-predictive mode, a weighted average of motion-compensated list 0 and list 1 prediction signals is used for the forecast signal. The direct prediction mode is inferred from previously transmitted syntax elements and can be any of the other types of modes. For each 16 x 16, 16 x 8, 8 x 16, and 8 x 8 panel, list 0, list 1 or bi-predictive methods can be chosen separately. A 8 x 8 partition of a B macroblock can also be coded in straight mode. Similar to P-Skip mode, if no prediction error signal is transmitted for a straight macroblock mode, it is also referred to as B-Skip mode. H.264 uses three transforms depending on the type of outstanding data that is to be coded: A Hadamard alter for the 4 x 4 array of luma DC coefficients in Intra-16 x 16 mode, a Hadamard alter for the 2 x 2 array of chroma DC coefficients and a DCT-based integer transform for all other 4 x 4 blocks in the residual data. Thanks to the integrated change, inversetransform mismatches are avoided. All inverse transform operations in H.264 can be realized using only additions and bit-shifting operations of 16-bit integer values. A quantization parameter (QP) is used in quantization
procedure which can take 52 different values on a macroblock basis. These values are arranged so that a raise of one in QP means an increase of quantization step size by approximately 12%. Rather than even increment, the step sizes increase at a compounding rate. This feature is not there in prior standards and it is of great importance for compression efficiency. In H.264, two methods of entropy coding are supported. The first one is Context-Adaptive Variable Length Coding (CAVLC) and the other one is Context-Adaptive Binary Arithmetic Coding (CABAC). In CAVLC, VLC tables for various sentence structure elements was switched depending on already transmitted syntax elements. As VLC tables are designed for competition the corresponding conditioned statistics, the entropy coding presentation is superior to the schemes using a single VLC table. CABAC improves the coding efficiency further (approximately 5-15% bit saving) by means of context modeling which is a procedure that adapts the probability model of arithmetic coding to the changing statistics within a video frame. In this procedure, conditional probabilities of the coding symbols and intersymbol redundancy can be subjugated as well. There are three Profiles in the standard. The Baseline outline supports I and P slices, and entropy coding with CAVLC. Also, it exploits redundant slices and arbitrary slice ordering (ASO) for error resilient coding. Potential submissions are video telephony, videoconferencing and wireless communications. The Main Profile includes hold for interlaced video, B slices, inter coding using weighted forecast and entropy coding with CABAC. Well suited application areas are television broadcasting and video storage. The Extended Profile does not hold interlaced video or CABAC but includes SP/SI slices to enable competent switching and data partitioning for improved error resilience. This profile may be particularly useful for streaming media applications. Unlike MPEG-2, MPEG-4 part 2 or H.263, H.264 currently does not hold layered scalable coding. Furthermore, unlike MPEG-4 part 2, it does not hold an object-based video or object based scalable coding . The focus of the typical is achieving higher coding efficiency. Thus, it consists of a large number of tools designed to address competent coding over a wide variety of video material. III. DETAILS OF THE COMPRESSION SCHEME To adapt to the H.264 intraframe coding, the two proposed methods are developed as two intracoding modes: RSQ and BCIM. They will be discussed in detail in this section. A. RSQ Mode
For text and graphics blocks containing edges of many directions as shown in Fig. 1, intraprediction along a single direction cannot completely remove the directional correlation among samples. After intraprediction, residues still preserve strong anisotropic correlation. In this case, it is not efficient to perform a transform on them. One method is to skip the transform and directly code prediction residues, which is similar to traditional pulse-code modulation (PCM). However, the question is whether the performance of PCM is better than that of a transform for text and graphics residual blocks. To
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014 answer it, we introduce the method proposed to analyze the coding gain of PCM over a transform. Given the same rate , the coding gain is defined as the ratio of distortions on transform coefficients and residual samples,respectively
GPCM /TC =
DPCM is the distortion of PCM and DTC is the distortion on transform coefficients. We assume the distortions result from the optimal quantization. Considering a stationary source, each sample will have the same variance σ S , DCPM is equal to
DCPM =∈s 2 σ s 2 2 −2 R
∈s is a factor that depends on the probability distribution function ρ (S ) of signal S and it is defined as 1 2 1 ∞ 3 ∈s = ( ∫−∞ ρ (S) 3 ds ) (3) 12 ρ (S ) = σ s ρ (σ s s ) DTC is equal to 1/ N
N −1 DTC = ∏∈t2,kσ t2, k k =0
∈t , k is a factor that has a similar form to (3) and it depends on the probability distribution coefficient, meanwhile
Ρk ( t ) of the th transform
σ t , k corresponds to its variance.Nis
the number of transform coefficients in a block, and it equals 64 for 8 X 8 size blocks Substituting (4)–(6) into (3), we have
Thus, the coding gain can be obtained numerically in a statistical sense. B. BCIM Mode
Having limited colors but complicated shapes is another property of the text and graphics parts on images. Such text/graphics blocks can be expressed concisely by several base colors together with an index map. It is somewhat like color quantization that is a process of choosing a representative set of colors to approximate all the colors of an image . In the BCIM mode, we first get the base colors of a
block by using a clustering algorithm. All the base colors constitute a base color table. Then, each sample in the block will be quantized to its nearest base color. The index map indicates which base color is used by each sample. Different from color quantization, each text/graphics block, but not an entire image, has its own base colors and an index map for representation in our scheme. Thus, it is content adaptive for each block. In addition, since the base color number of a block is small, fewer bits are required to represent each mapped index. Let us take the luminance plane of a 16 16 text/graphics block as an example, with each sample expressed by 8 bits. If four base colors are selected to approximate colors of that block, only two bits are required to represent each sample’s index without compression. Thus, the total bits required to represent the block can be reduced from to, saving almost 3/4 of the bits. Taking chrominance components of a block into account, the base colors and an index map representation can also be applied on a vector block consisting of three color components such as that in the color space. In this case, each base color is a vector but only one index map is required for that vector block. For convenience, we will refer to the representation on a vector block as Dim3 and that only on luminance with chrominance components coded by conventional method as Dim1. The Dim3 case is efficient for blocks having high correlation among three color components and the Dim1 case is efficient for those with low correlation among them. In this mode, there are several key problems to solve. First, base colors should be chosen to well represent a block. Second, after base colors are selected, there should be a well designed entropy coding method to code the base colors and index map.If the coding method has been decided and is good enough, the problem is then simplified as the searching of the best base colors for that block. C. Mode Selection and Mode Structure
Each mode has its advantages at dealing with blocks of different features. One question that arises here is how to fully take advantage of each mode in the proposed scheme. It can be solved by the RDO algorithm that has been adopted by H.264.The best mode with the best block partition having the minimum rate-distortion cost will be selected to compress the currentblock. All modes in the proposed scheme can be categorized into two types: spatial domain (SD) and DCT frequency domain (FD).There is a flag in the bit stream to distinguish them. The organized structure of all the modes. FD indicates the original intramodes in H.264, where the compression is performed in the DCT domain. SD indicates our proposed RSQ and BCIM modes. To adapt to the local nonstationary property ofcompound images, the spatial domain (SD) modes are applied to 16 X 16, 8 X 8, and 4 X 4 block sizes as those DCT frequency domain (FD) modes. The best mode in the spatial domain is compared with the best mode in the DCT frequency domain for the same size block in the rate-distortion sense. The better one is selected. For 8 X 8 and 4 X 4 size blocks, 8 prediction directions are designed for the RSQ mode as shown in Fig. 3(a). The DC mode in the spatial domain is replaced by the BCIM mode in the stream syntax. For those small size blocks, the BCIM
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.8 AUGUST 2014 mode is only performed on the luminance component in our scheme for simplicity.In 16 X 16 blocks, the BCIM mode takes the place of the DC intramode. The better one between Dim3 and Dim1 is selected based on the rate-distortion criteria. For the Dim3 case, when the input image format is YUV 4:2:0, interpolation will be performed on UV color planes to get the same size color planes to facilitate the 3-D clustering. To be consistent with the block size of the DCT transform, the selection between direct quantization and transform coding on the 16 X 16 residual block, which is obtained by the prediction with three possible directions,is performed on 4 X 4 sub blocks. IV. EXPERIMENTAL RESULTS The H.264/MPEG-4 standard is quite different and more flexible when compared to older video coding standards in terms of picture coding order, group of picture organization and assignment of reference pictures. In contrast to older standards, the coding and display order of the pictures is completely decoupled. Thanks to these features,H.264/MPEG-4 not only gives a better compression efficiency but also enables temporal scalability. In this section, performance results are reported for certain test sequences under several picture coding order and organization patterns.
A frame from BCIM MODE sequence
b) A frame from RSQ MODE sequence
A frame from OUTPUT MODE sequence
The hierarchical B picture concept is only examined in terms of compression efficiency in here. As far as temporal scalability is concerned, other issues such as reference selection should be considered. The reference picture lists have to be selected in a way that only pictures that belong to a coarser or same temporal level as the current picture are included in the reference picture lists. V. CONCLUSION The video signal has high temporal redundancies between a number of frames and this redundancy has not been exploited enough by current video compression technics. Two spatial domain modes, called residual scalar quantization (RSQ) and base colors and the index map (BCIM), are integrated into H.264 intraframe coding in this research. They are both able to preserve the spatial structures of the text and graphics parts, important to visual quality. A rate distortion optimal method, similar to that in H.264, simplifies the mode selection and avoids the performance loss imported by the inaccurateness of segmentation.With the apparent gains in compression efficiency we foresee that the proposed method could open new horizons in video compression domain; it strongly exploits temporal redundancy with the minimum of processing complexity which facilitates its implementation in video embedded systems. It presents some useful functions and features which can be exploited in some domains as video surveillance. In high bit rate, it gives the best compromise between quality and complexity. It provides better performance than MJPEG and MJPEG2000 almost in different bit rate values. Over .2000kb/s. bit rate values our compression method performance becomes comparable to the MPEG 4 especially for low motion sequences. There are various directions for future investigations. REFERENCES . Cuiling Lan, Guangming Shi, and Feng Wu “Compression Compound images in H.264/MPEG-4 AVC By Exploiting Spatial Correlation”, IEEE Transactions On Image Processing, VOL. 19, NO. 4, APRIL 2010. . ITU-T and ISO/IEC JTC 1, “Generic coding of moving pictures and associated audio information - Part 2: Video”, ISO/IEC 13818-2 (MPEG-2), 1994. . A. Puri, X. Chen, A. Luthra, “Video Coding Using the H.264/MPEG-4 AVC Compression Standard”, to be published in Elsevier Science, Signal Processing: image Communication, September 2004 issue.
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International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711) August 2014 Issue Vol.4 No.8