A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

Transcription

1 Regular Issue A Review on Block Maching Moion Esimaion and Auomaa Theory based Approaches for Fracal Coding Shailesh D Kamble 1, Nileshsingh V Thakur 2, and Preei R Bajaj 3 1 Compuer Science & Engineering, Yeshwanrao Chavan College of Engineering, India 2 Compuer Science & Engineering, Prof Ram Meghe College of Engineering & Managemen, India 3 Elecronics Engineering, G H Raisoni College of Engineering, India Absrac --- Fracal compression is he lossy compression echnique in he field of gray/color image and video compression I gives high compression raio, beer image qualiy wih fas decoding ime bu improvemen in encoding ime is a challenge This review paper/aricle presens he analysis of mos significan exising approaches in he field of fracal based gray/color images and video compression, differen block maching moion esimaion approaches for finding ou he moion vecors in a frame based on iner-frame coding and inra-frame coding ie individual frame coding and auomaa heory based coding approaches o represen an image/sequence of images Though differen review papers exis relaed o fracal coding, his paper is differen in many sense One can develop he new shape paern for moion esimaion and modify he exising block maching moion esimaion wih auomaa coding o explore he fracal compression echnique wih specific focus on reducing he encoding ime and achieving beer image/video reconsrucion qualiy This paper is useful for he beginners in he domain of video compression Keywords --- Fracal Compression, Compression Raio, Encoding Time, Decoding Time, Auomaa, Moion Esimaion I Inroducion ih he mos challenging area in compuer animaions and Wmulimedia echnology, daa compression remains a key issue regarding he cos of sorage space and ransmission imes Fracal Compression was firs promoed by M Barnsley which is based on Ieraed Funcion Sysem (IFS) [1][2] I basically deals wih he exploraion of he self-similariy presen in he given image Though he Fracal coding is advanageous wih respec o he compression raio and image reconsrucion qualiy, bu i has he heavier non-accepance relaed o he ime elapsed for he check of similariy I is suiable for he gray level image compression, bu laer some new echniques were also developed for he color image/video compression The collage heorem [3] is he basis for he fracal ransform The collage heorem for an inpu image I, a new se W(I) is compued by he union of n number of sub-images, each of which is formed by applying a conracive affine ransformaion w i on I as given in (1) A pracical realiy was given o fracal compression by Jacquin wih pariioned IFS (PIFS) [3] Video compression deals wih he compression mechanism for he series of image sequences In coding, he correlaion beween he adjacen image frames may ge explored, as well as he relaiviy beween hem may also be used in he developmen of he compression mechanism Generally, he adjacen image frame does (1) no differ much The probable difference lies in he displacemen of he objec in he given image frame wih respec o he previous image frame Grey and color videos (ie image sequences of gray or color image frame) are he cusomers for he video compression approach Differen color spaces [4] can be used for he video images from he processing poin of view Wih he very fas developmen in mulimedia communicaion wih moving video picures, processing on color images plays a very imporan role A color image is represened by 24 bis/ pixel in RGB color space forma, wih each color componen represened by 8 bis Block maching moion esimaion is a popular echnique for many moion compensaed video coding sandards Video compression sandards, in general, are used for he video coding Basic video compression sandards are- Video coding sandards are relaed o he organizaions- ITU-T Rec H261, ITU-T Rec H263, ISO/IEC MPEG-1, ISO/IEC MPEG-2, ISO/IEC MPEG-4, and recen progress is H264/AVC In a series of he image sequence, here are spaial, emporal and saisical daa redundancy ha arises beween frames Moion esimaion and compensaion are used o reduce emporal redundancy beween successive frames Moion esimaion compues he moion/movemen of an objec in a given image For achieving daa compression in a sequence of images, he moion compensaion uses he knowledge of he moion objec Differen searching echniques are available o compue he moion esimaion beween frames A finie auomaon is a mahemaical model used in he heoreical foundaion of compuer science o show accepance or rejecion of a paricular sring of an algorihm Transiion diagram, ie graph, is a convenien way of designing finie auomaa Any image can be represened by a finie auomaon Because of is simple mahemaical srucure, a finie auomaion is used in fracal image/ video compression The principle of finie auomaa is based on he self-similariy presen wihin he picure iself Image compression wih finie auomaa can also be applied o digial video sequences, which are ypically represened by a series of frames or digial images [5] The concep of finie auomaa is now generalized o weighed finie auomaa The remainder of his paper is organized as follows: An overview of fracal Image compression is given in secion 2 Secion 3 describes he relaed work on fracal image coding An overview of fracal video coding is given in Secion 4 Secion 5 describes a relaed work on auomaa heory based coding Secion 6 focuses on work carried ou on fracal video coding using block maching moion esimaion This secion elaboraes he differen moion esimaion approaches and Secion 7 discusses abou he qualiy measures associaed wih video processing Secion 8 summarizes he conclusion from he sudied exising approaches Finally, he paper ends wih he fuure scope in secion 9 followed by references DOI: /ijimai

2 Inernaional Journal of Ineracive Mulimedia and Arificial Inelligence, Vol 4, Nº2 II Overview of Fracal Image Coding In general, here are wo ypes of compression Lossless and Lossy In lossy daa compression echniques some amoun of he original daa is los during he compression process For fas ransmission of images across he inerne media lossy echniques are used in World Wide Web Examples of lossy echniques are JPEG, GIF, Wavele, Fracal, DCT ec In lossless daa compression echniques very few amoun of daa is los Examples of lossless echniques are: TIFF, CCD RAW, ec Fracal coding echniques are generally applied on gray level images For color image compression, each of red, green and blue componen is compressed individually using gray image fracal coding algorihm Fracal coding is a block-based processing echnique which akes long encoding ime for compression bu less decoding ime for decompression and i falls in he caegory of lossy compression echnique In basic fracal image coding, he original image is divided ino small non-overlapping range block (R block) of fixed size which is nohing bu a group of a collecion of horizonal and verical pixels For each R block, find overlapping domain block (D block) of fixed size which is also a group of a collecion of horizonal and verical pixels and are generally wo or four imes he size of he range block For insance, If he image of size I=2 N x 2 N is divided ino non-overlapping range block R i=0,1,2, m of size 2 n x 2 n, hen search for he bes mach overlapping domain block D j=0,1,2, n of size 2 n+1 x 2 n+1 ie double he size of range block The number of range block for a single plane is R m = (2N x 2 N ) / 2 n x 2 n and he number of domain block for a single plane is D n = ( (2 N x 2 N ) (2 n+1 x 2 n+1 ) + 1) 2 Domain pool D P consiss of all he ransformed and under-sampled domain block such ha i maches o he size of he range block Block schemaic of basic fracal coding encoder is shown in Fig 1 The seps for basic fracal encoding algorihm [6] are as follows 1 Divide he original image o be encoded o ge he non overlapping range blocks R i 2 Divide he original image o be encoded o ge he overlapping Domain blocks D j 3 Generae domain pool D P consising of all ransformed and shrink domain blocks D j 4 For each domain block D j in domain pool D p, using leas square regression mehod [3] given in equaion (2) and (3), compue he values of conras facor-s and brighness facor-o by referring R i and D j Given a pair of range block R i and ransformed and shrink domain block D j of n pixels wih inensiies r 1, r 2 r n and d 1, d 2, d n o minimize he quaniy ie R Fig 1 Schemaic of Basic Fracal Coding Encoder Mechanism The decoding of a compressed image can be easily achieved wih a saring value B (0) given in relaion (5) B (q) = W(B (q-1) ) (5) Where W is Fracal Transformaions applied on each domain block, q is number of ransformaions ie Eigh affine/linear ransformaions and {B (0), B (1), B (2),B (q-1), B (q) } se of sequence of ransformaion The Peak-signal-noise-raio(PSNR) value describes he image qualiy of he decoded image compued by using equaion (6) PSNR = (6) where (2) and (3) 5 Compue error E(R i, D j ) using equaion (4) and Quanize facor s and o using uniform quanizer = if =0 hen s =0 and o = 6 Search all domain blocks D j for a paricular range block o be encoded R and find he mos suiable block i D j wih minimal error (R i, D j ) = min E(R i,d p ) (4) Where A is ampliude of he signal and is given as A=2 8-1=255 8-bi gray image and MSE is compued as follows MSE= (7) Where X i,j and Y i,j are he pixel (i, j) inensiies of original and decoded image respecively In sequenially approximaing each range block by suiable domain block, he major drawback is exhausive searching ha is required for finding a bes maching domain block, which leads o increase encoding ime Several approaches have been proposed for reducing encoding ime In his review paper, we presen various approaches for reducing encoding ime for he image as well as video fracal coding Classificaion of fracal image and video coding approaches is represened using Block schemaic as shown in Fig 2

3 Regular Issue Fig 2 Classificaion of Fracal Image and Video Coding Approaches III Relaed Work on Fracal Image Coding Lieraure available on Fracal gray image compression is very large and few repor abou he color image compression Formulaion of approximae neares neighbor search in [7] is based on orhogonal projecion and prequanizaion of he fracal ransforms parameers Ghosh e al [8] searched domain blocks randomly for every range block o minimize he encoding ime Truong e al [9] opimized he range blocks pool and domain blocks pool using spaial correlaion o minimize he search space and searching ime The encoding algorihm in [10] based on he law of cosines Fan and Liu [11] presened he maching algorihm based on he Sandard Deviaion (STD) beween range blocks and domain blocks Wang e al [12] proposed Correlaion informaion feaure o find neares neighbor domain block for each range block Ghosh e al [13] proposed an approach for fracal image coding based on an innovaive concep of relaive fracal coding which found o be suiable for coding muli-band saellie images Conci and Aquino [14] used fracal dimensions for he image par classificaion where firs he image block fracal dimension complexiy is evaluaed and hen only pars wihin he same range of complexiy are used for esing he beer self-affine pairs Li e al [15] presened a kernel funcion clusering based on an an colony algorihm I auomaically realizes classificaion of he domain block Harensein e al [16] presened boom-up region merging approach where regions are merged on he basis of collage error Belloulaa and Konrad [17] proposed an approach o fracal image coding ha permis region-based funcionaliies where images are coded region by region according o a previously-compued segmenaion map The mehod in [18] is paricularly well suied for use wih highly irregular image pariions for which mos radiional (lossy) acceleraion schemes lose a large par of heir efficiency Franco and Mala [19] presened an algorihm for adapive image pariioning achieving designaed raes under a compuaional complexiy consrain Wang [20] proposed a graph based image segmenaion algorihm used o divide he image ino he differen logical area and each logical area is coded ino adapive hreshold quadree pariioning approach Hassaballah e al [21] minimized domain pool size on he basis of enropy value of each domain block Domain pool reducion is parameerized and non-adapive by allowing an adjusable number of domains o be excluded from he domain pool based on he enropy value of he domain block He e al [22] uilized one-norm of a normalized block o minimize he domain pool search space, in which he search process migh be erminaed early, and hus remaining domain blocks could be safely discarded Rowshanbin e al [23] used a special characerisic vecor o classify he domain blocks o minimize he searching ime The approach in [24] uses he minimum disorion and variance difference beween he range block and domain block o minimize he domain pool and searching ime To speed up he encoding, in [25], he classificaion feaures are used o classify he image blocks Xing e al [26] used hierarchical pariioning o classify he domain pool Fuzzy paern classifier is uilized in [27], o classify he original image blocks Qin e al [28] sored domain pool on a number of hopping and he variance of coninuing posiive and negaive pixels in he given block The approach in [29] reduces he memory requiremen, and speeds up he reconsrucion In Sochasic image compression using Fracals [30], sochasic image coding based on he fracal heory of ieraed conracive ransformaions is discussed Fracal image compression based on he heory of ieraed funcion sysem (IFS) wih probabiliies is explained in [31] Gungor and Ozurk [32] discussed a hash funcion based image classificaion echnique No search fracal image compression in DCT domain is discussed in [33] Eugene and Ong [34] discussed wo pass encoding scheme o speed up he encoding process The approach in [35] is based on compuing he gray level difference of domain and range blocks Peng e al [36] obained he range blocks by pariioning he image using adapive quad rees A direc allocaing mehod o predic he desired ransformaion for similariy measure is discussed in [37] Liu e al [38] presened he enropy based encoding algorihm Melnikov [39] presened he mehod of acceleraion of he image fracal coding Disasi e al [40] proposed an approximaion error based approach o classifying he blocks The fundamenal idea of his approach consiss in deferring range and domain comparisons, based on feaure vecors A prese block, as a emporary replacemen wih which range and domain blocks are compared There are many color spaces [4] available o represen he color images To display he image on he monior, he RGB color space is generally used For color image compression, he RGB model is bes suied because i provides he highes correlaion [41] For color image compression on square archiecure, he fracal coding is applied on differen planes of a color image independenly by reaing each plane as a gray level image This approach is Sraigh Fracal Coding or Separaed Fracal Coding (SFC) [42] Work relaed o fracal coding of color images repored in he lieraure are: Hurgen e al discussed a fracal ransform coding of color images in [43] To exploi he specral redundancy in RGB componens, he roo mean square error (RMSE) measure in grey-scale space is exended o 3-dimensional color space for fracal-based color image coding in [44], where i is claimed ha 15 compression raio improvemen can be obained using vecor disorion measure in fracal coding wih fixed image pariion as compared o separae fracal coding in RGB images Comparaive sudy of fracal color image compression in he L*a*b* color space wih ha of Jacquin s ieraed ransform echnique for 3-dimensional color is presened in [45], where i is claimed ha he use of uniform color space has yielded compressed images wih less noiceable color disorion han oher mehods Li e al [46] presened fracal color image compression scheme based on he correlaion beween he hree planes of RGB color space Giuso e al presened an approach for color image coding based on he join use of he L*a*b* color space and Earh Mover s Disance in [47] Thakur e al [6] discussed a Fracal compression echnique, which is basically a searching echnique based on self-similariy search wihin an image and elaboraed basic seps

4 Inernaional Journal of Ineracive Mulimedia and Arificial Inelligence, Vol 4, Nº2 required for Fracal coding echnique ie pariioning ino rang/domain blocks, searching each range block wih all domain blocks and sores he values of bes ransformaions Thakur and Kakde [51] proposed a modified fracal coding approach on spiral archiecure o opimize domain blocks using local search One of he major challenges in fracal coding echnique is o opimize/reduce he size of domain pool Reducing he number of domain blocks in a domain pool reduces encoding ime in fracal coding echnique IV Overview of Fracal Video Coding There are wo mehods for fracal video: coding-frame based and Cube based fracal video coding In frame based coding, each single frame of image is pariioned ino a range and domain square block, which is already described in secion 2, and hen each frame is coded based on iner-frame and inra-frame coding o reduce he redundan par of daa wihin a frame and beween wo frames using fracal ransformaions The iner-frame and inra-frame coding are discussed in secion 41 Every range block of he curren frame is encoded by he domain block from he previous frame ie encode every range block by using iner-frame similariy as shown in Fig 3 The error occurs in he laer/nex frames due o he iner-frame similariy, ie use of domain block from he previous frame, and delay occurs beween wo successive frames during he processes of decoding, which are he major drawbacks in frame based video coding This mehod is advanageous in achieving a high compression raio and hus i is used in video ransmission hrough he inerne/www media In cube based coding, The video sequence o be coded is firs combined ino a group of frames(gof) and hen each GoF is pariioned ino a se of range and domain cubes as shown in Fig 4 For insance, image can be viewed as 3D /cubic digial image of size I=2 N x 2 N x 2 N, which is divided ino non-overlapping range block R i=0,1,2, m of size 2 n x 2 n x 2 n hen search for he bes mach overlapping domain block D j=0,1,2, n of size 2 n+1 x 2 n+1 x 2 n+1 ie double he size of range block The number of range blocks for a single plane is R m = (2N x 2 N x 2 N ) / 2 n x 2 n x 2 n and he number of domain blocks for a single plane is D n = ((2 N x 2 N x 2 N ) (2 n+1 x 2 n+1 x 2 n+1 ) + 1) 2 Domain pool D P consiss of all he ransformed and under-sampled domain blocks such ha i maches he size of he range blocks as shown in Fig 5 If N = 2 and n = 1, hen for a cubic digial image of size I=4 x 4 x 4 having a number of pixels 64, he number of range blocks R m = 8, having a number of pixels in each range block ha is 8, and he number of domain blocks Dn = 1, having a number of pixels in domain block ha is 64 This mehod is advanageous in obaining a high qualiy of decoded image in receiving end bu disadvanageous in achieving a low compression raio Fig 3 Rang and Domain Blocks Formaion in Frame based Video Coding Fig 5 Domain Cube Maching wih Range Cube The basic fracal encoding algorihm for sill images given in secion 2 is easily exended o fracal video coding The seps for obaining frame based fracal video coding are as follows 1 Exrac image sequences from video F f = {F 1,F 2, F n } 2 For each frame F f { 2 f n} do 3 Apply fracal coding for sill image F f-1 o compue decoded image DF f-1 4 Divide he frame F f o ge he non overlapping Range block R i 5 Divide he decoded frame DF f-1 o ge he overlapping domain block D j 6 Generae domain pool D P from decoded frame DF f-1 consising of all ransformed and shrink domain blocks D j 7 For each domain block D j in domain pool D p, using leas square regression mehod [3] given in equaion (2) and (3) compue he values of conras facor-s and brighness facor-o by referring R i and D j in domain pool DP Given a pair of range block R i and ransformed and shrink domain block D j of n pixels wih inensiies r 1, r 2 r n and d 1, d 2, d n o minimized he quaniy ie R 8 Compue error E(R i, D j ) using equaion (4) and Quanize facor s and o using uniform quanizer 9 Searching all domain block D j for a paricular range block o be encoded R and find he mos suiable block i D j wih minimal error (R i, D j ) = min E(R i,d p ) Similarly a basic fracal encoding algorihm for sill image is easily exended for obaining cube based fracal video coding are as follows 1 Exrac image sequences from video F f = {F 1,F 2, F n } 2 Frame sequence is divided ino GoF i = {GoF 1,GoF 2,GoF n } 3 For each GoF i do 4 Divide he GoF i o ge he non overlapping range cube block R i 5 Divide he GoF i o ge he overlapping domain cube block D j 6 Generae domain pool D P from GoFi consising of all ransformed and shrink domain blocks D j 7 For each domain block D j in domain pool D p, using leas square regression mehod [3] given in equaion (2) and (3) compue he values of conras facor-s and brighness facor-o by referring R i and D j Given a pair of range block R i and ransformed and shrink domain block D j of n pixels wih inensiies r 1, r 2 r n and d 1, d 2, d n, o minimized he quaniy ie R Fig 4 Range and Domain Cubes Formaion in Cube based Video Coding Compue error E(R i, D j ) using equaion (4) and Quanize facor s and o using uniform quanizer

5 Regular Issue 9 Searching all domain blocks D j for a paricular range block o be encoded R and find he mos i suiable block D wih minimal error (R j i, D j ) = min E(R i,d p ) A Inra-frame and Iner-frame Coding In video coding here are wo ways o reduce he daa presen in he frames Firsly, spaial redundancy eliminaion, which is called inra-frame coding, in which he frames are coded individually as done in JPEG compression echnique Wihin individual frame coding, similar daa par can be coded wih fewer bis per pixels han he original daa par, herefore reducing bis per pixel is a minor loss in noiceable visual qualiy of individual frame coding On he oher hand, emporal redundancy eliminaion, which is called iner-frame coding, in which he redundan daa are eliminaed beween he frames as done in MPEG compression echnique Iner-frame coding finds he difference beween he previous frame and curren frame and sores only difference ie displacemen of pixels insead of complee frames The displacemens of pixels are esimaed by using a well-known echnique called block maching moion esimaion Block maching moion esimaion echniques are used o find ou he moion vecors in a frame and hen he displacemen of pixels block idenified by moion esimaion echnique is coded Laer his block is considered as a previous frame and he nex frame in a sequence is considered as a curren frame for finding ou he difference This coding process is repeaed for all he remaining frames in a video sequence Fig 6 shows he block schemaic of inra-frame and iner-frame coding echniques q 1 Є Q and R is he real number ie weigh beween sae q 0 and q 1, he iniial configuraion (I) of saes Q R and indicaes which saes corresponds o he enire image I(q 0 )=1 and I(q i )=0, q 0 q i where q 0, q i Є Q and i=1,2,3n, he final configuraion (F) of saes Q R eg F (q 0 ) = f(є) where q 0 Є Q and f(є) is he average inensiy (Greyness) of he enire image, and he iniial sae q 0 of he WFA means he enire original image ie q0 Є Q The exended version of weighed finie auomaa is called as exended weighed finie auomaa (EWFA), which is similar o WFA in which all he subimages in EWFA are ransformed using ransformaion funcion ie scaling and roaion ec Similarly o WFA, EWFA is also used o sore and compress daa represened as an image/marix In EWFA he numbers of saes o sore he subimages/marix daa are less as compared o WFA and herefore memory space required o sore he saes are less TABLE I Summary of Video Coding Sandards wih Objecives/ Properies Sandard Organizaion Sandards Properies MPEG-1 MPEG-2 ISO/IEC ISO/IEC Main applicaion is mulimedia video sorage, video frame rae - 24 o 30 fps, bi rae- 15 Mbps, image forma- CIF and no inerlace TV resoluion- NTSC:704 x 480 Pixel, digial cable TV disribuion similar o NTSC, PAL, SECAM a 4-8 Mbps, HD elevision a 20 Mbps and inerlace MPEG-4 ISO/IEC Objec based coding and used in wide range of applicaions ie TV film, video communicaion, video cameras, Inerne sreaming, mobile phones ec wih choices of ineraciviy, scalabiliy, error resilience, ec Fig 6 Inra-frame (Spaial) and Iner-frame (Temporal) Coding Technique B Video Coding Sandards Video compression sandards, in general, are used for he video coding Basic video compression sandards are- Video coding sandards are relaed o he organizaions are- ITU-T Rec - H261, H263, ISO/ IEC- MPEG-1, MPEG-2, MPEG-4, and H264/AVC Summary of video coding sandards wih heir properies are given in Table I Block maching moion esimaion algorihms play an imporan role in designing video coding sandards Video coding sandards consis of moion esimaion algorihm, encoding mechanism and decoding mechanism o eliminae redundan daa C Preliminaries of Auomaa Theory In general, a finie auomaon is a simple mahemaical model used o recognize he srings generaed by regular expression noaion In image / video compression echnique, finie auomaa are used o represen he enire image and he address of each subimages of he enire image is specified by he regular expression ie * = {0, 1, 2, 3}* where 0, 1, 2 and 3 are he address of he subimages Finie auomaa coding process is similar o he fracal coding process by exending finie auomaa model wih he weighed finie auomaa (WFA) model WFA is an exended version of finie auomaa in which a weighed ransiion is associaed beween he wo saes A WFA M= (Q,, W, I, F, q 0 ) consiss of a finie se of saes ie Q= {q0, q1, q2 qn}, a finie se of inpu symbol/quadran address ie = {0,1,2,3} for Quadree WFA, = {0,1} for Binree WFA and = {0,1,2,3,4,5,6,7,8} for Nonaree WFA, he weigh funcion beween wo saes ie W Є Q Q R, for W (q 0, q 1 ) = R where q 0, H261 and H263 H264 ITU-T ITU-T and ISO/IEC Video conferencing applicaions over ISDN elephone lines Broadcas over cable, modems, ADSL, erresrial ec Opical / magneic sorage devices and DVDs sorages Video-on-demand or sreaming service and Mulimedia messaging service (MMS) over ISDN, LAN, Eherne, modems, wireless and mobile neworks ec, or heir combinaions V Relaed Work on Auomaa Theory Weighed Finie Auomaa (WFA) is a generalizaion of finie auomaa by aaching real numbers as weighs o saes and ransiions proposed by Culik and Kari [49-50] WFA provides a powerful ool for image generaion and compression The inference algorihm for WFA subdivides an image ino a se of non-overlapping range images and hen separaely approximaes each one wih a linear combinaion of he domain image A new predicive video-coding echnique [50], using fracal image compression for inra-frame coding and second order geomeric ransformaions for moion compensaion in iner-frame predicive coding, is proposed For moion compensaion second order geomeric ransformaions, compensaing for ranslaion, roaion, zooming, uneven sreching, and any combinaion of hese, has been used The decoded images can be displayed a arbirary resoluion wihou blockiness, which, ogeher wih he very high compression raio achievable, is he mos imporan advanage of he fracal-based image coding echnique over he sandard discree cosine ransform (DCT)-based image coding [50] WFA is one of he echniques ha have been used o compress digial images [51-52] WFA represens an image in erm of a weighed finie auomaon wih a very good

6 Inernaional Journal of Ineracive Mulimedia and Arificial Inelligence, Vol 4, Nº2 compression raio WFA is based on he idea of fracal ha an image has self-similariy in iself In his case, he self-similariy is sough from he symmery of an image, so he encoding algorihm divides an image ino muli-levels of quad-ree segmenaions and creaes an auomaon from he sub-images [53] As he developing of he fracal image compression, he fracal coding mehod has been applied in video sequence compression [54], for insance, he famous hybrid circular predicion mapping and non-conracive iner-frame mapping [55] The circular predicion mapping / non-conracive iner-frame mapping combines he fracal coding algorihm wih he well-known moion esimaion and compensaion algorihm ha explois he high emporal correlaions beween adjacen frames WFA codec is modified such ha i compresses he video a low bi-raes The video is he sequence of frames (images) Here a hierarchical moion compensaion (MC) [56] and bin-ree based WFA codec are inegraed o exploi he correlaion beween successive frames [57] The advanage is ha WFA encoding can replace oher ransformaion coding in video applicaions A low bi-raes, WFA encoded images are ypically half he size of comparable JPEG images Video coding scheme based on bi-plane modeling and GFA represenaion is used o explore he iner-frame, iner-bi plane and iner-level similariies presen in he video To form a generalized finie auomaa-based compac represenaion of video sequence, he GFA modeling akes advanages of he binary fracal similariy of he video sequence in he wavele domain Afer exploring he similariies we ge compac generalized finie auomaa represenaion of video [58-59] Such schemes significanly ouperform he H26X [60] series coding schemes in rae disorion performance and reain an accepable percepual qualiy of he video A generalized finie auomaon [61] is used o encode/decode he images auomaically When he GFA is combined wih he wavele ransform echnique, he GFA is consruced in such a way ha each sae represens one wavele funcion So his mehod combines he advanages of boh, he classical wavele compression mehod and GFA While encoding he image, GFA doesn have o solve equaions So encoding may ake significanly less ime Decoding of images can also be done more quickly This mehod of GFA allows any combinaion of roaions, flips, and complemenaion of he quadran image [62] Quad-ree based EWFA coding is used in fracal color video compression echnique [66] The quadree pariioning scheme is used o specify he address of each sub-images ie a complee quadree represens a pyramidal srucure of an enire image ha is required for he EWFA encoding and decoding process The concep of inra-frame coding ie individual frame coding is used o encode he number of frames in a video In Quadree based EWFA coding, each color frame is convered ino he YC b C r color space and hen convered ino gray scale [67, 68] Each gray scale frame is divided ino a fixed sized block (2 n 2 n ) based on quadree pariioning o represen he address of each pixel VI Relaed Work on Moion Esimaion In circular predicion mapping and non-conracive iner-frame mapping, each range block is moion compensaed by a domain block in he previous frame, which is of he same size as he range block even hough he domain block is always larger han he range block in convenional fracal image codec The main difference beween circular predicion mapping and non-conracive iner-frame mapping is ha circular predicion mapping should be conracive for he ieraive decoding process o converge, while non-conracive inerframe mapping needs no be conracive since he decoding depends on he already decoded frames and is non-ieraive [66] Recenly, Wang [67] proposed a hybrid fracal video compression algorihm, which merges he advanages of a cube-based fracal compression mehod and a frame-based fracal compression mehod; in addiion, an adapive pariion insead of fixed-size pariion is discussed The adapive pariion [68] and he hybrid compression algorihm exhibi, relaively, he high compression raio for image [68] and he video conference sequences [67] In conclusion, a fracal image codec performs beer in erms of very fas decoding process as well as he promise of poenially good compression [69-73] Bu a presen, he fracal codec is no sandardized because of is huge calculaion amoun and slow coding speed In order o alleviae he above difficulies, a block-maching moion esimaion echnology [74, 75] can be used o improve he encoding speed and he compression qualiy [76] Block-maching moion esimaion is a vial process for many moioncompensaed and video coding sandards Moion esimaion could be very compuaionally inensive and can consume up o 60%-80% of he compuaional power of he encoding process [77] So research on efficien and fas moion esimaion algorihms is significan Block maching algorihms are used widely because hey are simple and easy o be applied In he las wo decades, many block maching algorihms are proposed for alleviaing he heavy compuaions consumed by he brue-force full search algorihm which has he bes predicion accuracy, such as he new hree-sep search [78], he four-sep search [79], he block-based gradien descen search [80], he diamond search [81], he cross-diamond search [82], ec All hese searches employ recangular search paerns of differen sizes o fi he cener-biased moion vecor disribuion characerisics [83-84] Hexagon-based search employs a hexagon-shaped paern and resuls in fewer search poins wih similar disorion [85] Block-maching algorihm called Novel Cross-Hexagon Search algorihm is proposed in [75] I uses small cross-shaped search paerns in he firs wo seps before he hexagon-based search and he proposed halfway sop echnique [74] I resuls in higher moion esimaion speed on searching saionary and quasi-saionary blocks The radiional algorihms use all he pixels of he block o calculae he disorions ha resul in heavy compuaions Modified Parial Disorion Crierion [86] ha uses cerain pixels of he block, which alleviaes he compuaions and has similar disorion can be used New Cross-hexagon search algorihm (NHEXS) proposed in [87-88] consis of wo cross search paerns and hexagon search paerns which are similar in [75] for fas block maching Moion Esimaion This search echnique is a frame based fracal video compression echnique ha helps o reduce he encoding ime and increases he compression qualiy in fracal coding A Moion Esimaion Algorihms Moion esimaion used in he area of video applicaion such ha video segmenaion, objec/video racking, and video compression Moion esimaion means he displacemen of pixels posiion from one frame o anoher frame which gives he bes moion vecor For esimaing a moion in a video sequence, block maching algorihms (BMA) are widely used in mos of he video coding sandards In BMA a frame is divided ino a non overlapping block and for ha block moion vecor is esimaed A moion vecor is compued by finding bes suiable mached block beween previous frame-f and he nex frame-f+1 as shown in Fig 7 Moion vecor for he block B f (x, y) is compued as (+1,-1) ie MV-B f (x, y)=(+1,-1) Fig 7 Block Maching Moion Vecor Esimaion Sill, research is going on for efficien and fas block maching moion esimaion Differen ypes of block maching moion esimaion algorihms are described below

7 Regular Issue 1) Full Search Moion Esimaion This algorihm also called as exhausive search algorihm because moion vecor is compued afer a complee window of size (2w+1)*(2w+1) is exhausively searched for he purpose of bes maching of block size n x n pixels as shown in Fig 8(a) Due o he exhausive search, his algorihm gives a beer accuracy in searching he bes maching block The major drawback of his algorihm is o require a number of compuaions for he bes maching block The compuaional complexiy of his algorihm is very high when he window size is oo large and block size is oo small 2) Three Sep Search Koga e al [89] proposed a robus and very simple hree sep search moion esimaion algorihm This algorihm is one of he mos popular algorihms for he low bi rae applicaion because of is efficien performance Compuaional cos of his algorihm is low as compared o he full search algorihm Firs sep is o define a search window size for searching he bes mach In he firs sep, plo nine poins in he search window a he equal disance of sep size In he second sep, he sep size is divided by 2 if minimum block disorion measure (BDM) poin is one of he nine poin of search window and consider his poin as a cener poin in he hird sep This process is repeaed again unil he sep size is smaller han one This complee process is shown in Fig 8(b) If he sep size is s=7 hen he number of checking poins required for TSS is 25 using equaion given below Number of checking poins = log 2 (s +1) where s is sep size of he search window (a) (b) 1 s sep:, 2 nd sep:, 3 rd sep: Fig 8 (a) Full Search (b) Three Sep Search 3) 2D Logarihmic Search This algorihm was originally proposed by Jain and Jain [90] In his search sraegy, an iniial search window size is defined in he cenral area of he image As compared o full search moion esimaion algorihm, insead of searching all pixels in a complee search window, he search is done in five differen direcions which conain norh, souh, eas, wes and cenral direcion as shown in Fig 9(a), where Iniial window:, Sep size divided by 2:, Final sep: If he minimum BDM poin is found a any of hese five differen direcions hen his direcion is considered as a cener of he search window for he nex sep in which search window area is divided by 2 and his process is repeaed unil he search window area is convered ino 3x3 window size A las, all he nine poins are searched in he 3x3 search window and found minimum BDM poin corresponds o bes maching posiion ha gives he moion vecor ie block coordinaes 4) Orhogonal Search A Puri e al [91] inroduced an orhogonal search algorihm (OSA) based on he combinaion of hree sep search (TSS) and 2D logarihmic search algorihm This algorihm performs firsly a horizonal search wih 3 checking poins and secondly a verical search wih 3 checking poins including minimum BDM poin as a cener of he previous horizonal search The sep size is divided by 2 and his process is repeaed unil sep size is one as shown in Fig 9(b), where 1 s sep: Horizonal search, Verical search, 2 nd sep: Horizonal search, Verical search, 3 rd sep: Horizonal search h, Verical search If he sep size is s=7 hen he number of checking poins required for OSA is =13 using equaion given below Number of checking poins = log 2 ( s +1), where s is sep size of he search window (a) (b) Fig 9 (a) 2D Logarihmic Search (b) Orhogonal Search 5) Cross search M Ganbari [92] proposed a cross search paern algorihm (CSA) consising of 5 checking poins placed in a cross shape paern (Χ) In each sep find minimum BDM poin hen sep size is divided by 2, consider his poin as a cener and place 4 poins in cross shaped paern across he cener poin In he final sep, as he sep size is reduced o one, place cross search paern (+) if minimum BDM poin of he previous sep found a any one of he checking poin ie cener, upper lef corner and lower righ corner oherwise place cross search paern (Χ) as shown in Fig 10(a), where 1 s sep: :, 2 nd sep: :, 3 rd sep:, 4 h sep: If he sep size is s=7 hen he number of checking poins required for cross search algorihm (CSA) is =17 using equaion given below Number of checking poins = 5+ 4 log 2 ( s), where s is sep size of he search window 6) Binary Search Binary search (BS) Algorihm is used in MPEG-Tool [93] for block maching moion esimaion shown in Fig 10(b), where 1 s sep:, 2 nd sep: bes case, 2 nd sep:, average case, 2 nd sep:, Wors case Firsly his algorihm divides he 9 checking ouer poins of he search window ino small 9 search windows and, if minimum BDM poin is found a any one of he search windows, hen performs a search operaion on corresponding search window If minimum BDM poin is found a he cener, corner and middle of he search window hen he number of checking poins required for BSA is 25+9=33 for he wors case, 8+9=17 for he bes case and 14+9=23 for he average case respecively The pixels on he blue lines or he pixels beween he search windows are no considered for searching (a) Fig 10 (a) Cross Search (b) Binary Search (b)

8 Inernaional Journal of Ineracive Mulimedia and Arificial Inelligence, Vol 4, Nº2 7) New Three Sep Search R Li e al [78] proposed a new hree-sep search (NTSS) algorihm consising of wo searching paerns, ie cener biased checking paern and halfway-sop echnique (sep size divided by 2) like hree sep search algorihm as shown in Fig 11 This algorihm iniially defines a search window size for searching he bes mach and plos 17 poins on ha search window size as shown in Fig 11 From all he 17 poins, plo 9 poins on he 3x3 grid in he inner/cenral area of search window & res of he eigh poins plo on he 9x9 grid in he ouer area of he search window If he minimum block disorion measure is found a he cenre poin of he search window hen sop/hal searching, oherwise goo sep 2 In sep 2, if one of he cenral neighboring poins on 3x3 grid is found o be minimum BDM poin hen go o sep 3, oherwise goo sep 4 In sep 3, if minimum BDM poin is found a he corner poin or middle poin on he 3x3 grid in he inner/cenral area of he search window of he horizonal and verical axis, hen consider hese poins are cener poin and plo/search addiional 5 or 3 poins respecively in he search window In sep 4, if minimum BDM poin is found a corner poins and middle poins of every wo corners on he 9x9 grid in he ouer area of search window, hen sep size is divided by 2, his process is repeaed unil he sep size is smaller han one In his algorihm, he number of check poins required for NTSS is 17 for he bes case, 20 or 22 for average case and 33 for he wors case Cener biased checking: Sep 1, 2, & 3 Halfway sop echnique: Sep 4 Fig 11 New Three Sep Search Moion Esimaion 8) Four Sep Search Lai-Man Po e al [79] proposed a novel four-sep search (FSS) algorihm for block maching moion esimaion The performance measure of his search as compared o oher search algorihm is beer han TSS and similar o NTSS Insead of he 9x9 search window in NTSS, his algorihm uses a cenral biased search paern wih 9 checking poins on a 5x5 search window If minimum BDM poin is found a he cener of 5x5 search window, hen search window size is reduced o 3x3 window size ie final sep of FSS, oherwise goo nex sep In nex sep, if minimum BDM poin is found a any one of he four corners of he horizonal and verical axis or midpoins beween he wo corners of search window, hen check addiional 5 poins or 3 poins in he search window In he final sep, a search window of size 5x5 is reduced o 3x3 window size and minimum BDM poin is considered as a moion vecor as shown in Fig 12(a), where 1 s Sep:, 2 nd Sep: :, 3 rd Sep:, 4 h Sep: : If he sep size of he final sep is greaer han one, hen again anoher FSS is performed wih he final sep of he previous FSS is he firs sep of he anoher FSS The number of checking poins required for FSS algorihm is 9+8 = 17 for he bes case, = 27 for he wors case, and =23 for average case if minimum BDM poin found a cener poin in he firs sep, corner poin in he second and hird sep and midpoin in he second and hird sep of he search window respecively 9) Block- Based Gradien Descen Search LKLiu e al [80] proposed a block-based gradien descen search (BBGDS) algorihm based on cenral biased search paern of 9 checking poins This algorihm performs an unresriced search wihin a search window wih a sep size of one in each sep of block generaed search paern If minimum BDM poin is found a he corners of he horizonal and verical axis or mid poins of wo corners, hen checked addiional 5 or 3 poins respecively in he search window If he minimum BDM poin is found a he cener of curren block searched paern, hen searching will sop or, if he search paern reached o he search window boundary hen also sop searching This algorihm performs beer for small moions Search paern of block based gradien descen search algorihm are shown in Fig 12(b), where Upward MV: Sep 1:,, Sep 2:,, Sep 3: (a) (b) Fig 12 (a) Four Sep Search (b) Block-Based Gradien Descen Search 10) Diamond Search S Zhu e al [81] proposed a wo diamond shape search (DS) paern called large diamond shape search paern (LDSP) and small diamond shape search paern (SDSP) consising of nine and five checking poins including cener poin o form a diamond shape respecively This search iniially sars wih 9 checking poins ie LDSP, coninue searching wih his LDSP o form a new LDSP unil he minimum BDM poin found on he cener of he LDSP and hen shifed a large diamond shape search paern o small diamond shape search paern In he final sep of his search, he minimum BDM poin found in SDSP is considered as a final moion vecor as shown in Fig 13(a), where LDSP: 1 s Sep:, 2 nd Sep:, 3 rd Sep:, 4 h Sep:, SDSP : 5 h Sep: Final MV The number of checking poins required for Diamond search algorihm is 13 for he bes case The performance based on he checking poins compuaion of diamond search algorihm is closely relaed o efficien hree-sep search algorihm (ETSS) and beer han hree sep search (TSS), four-sep search (FSS) and blockbased gradien descen search algorihm 11) Cross Diamond Search CHCheung e al [82] proposed a cross diamond shape search paern algorihm based on halfway sop echnique for block maching moion esimaion This search algorihm iniially sars wih 9 checking poins cross shape (+) search paern insead of diamond shape search paern in he diamond search algorihm The performance based on checking poins of his algorihm is beer han he diamond search algorihm In he firs sep, if minimum BDM poin is found a he cener of large cross shape search paern (LCSP) ie 9 checking poins, hen sop searching In he second sep, search operaion is performed on small cross shape search paern (SCSP) ie 5 checking poins If minimum BDM poin is found a he cener of he SCSP, hen sop searching, oherwise in sep hree check anoher exra wo poins nearer o he minimum BDM poin found a he corner poins of SCSP If minimum BDM poin found a he cener of newly creaed SCSP, hen sop searching, oherwise perform a diamond search in he final sep as shown in Fig 13(b), where LCSP: 1 s Sep: :, SCSP: 2 nd

9 Regular Issue Sep:, 3 rd Sep:, Diamond search LDSP : &,, SDSP : Final Sep: Final MV reaches o search window boundary In his algorihm, he number of check poins required for Efficien Three Sep Search (ETSS) are 13 for he bes case and greaer han or equals o 29 for he wors case because of unresriced small diamond search paern a cenral par of search window as shown in Fig 15 (a) (b) Fig 13 (a) Diamond Search (b) Cross Diamond Search 12) Hexagon Search Ce Zhu e al [85] inroduced an hexagonal search algorihm for moion esimaion, which consiss of wo hexagon search paerns, Large Hexagon Search Paern (LHSP) wih 7 checking poins and Small Hexagon Search Paern (SHSP) wih 5 checking poins including cener poin of he hexagon as shown in Fig 14(a) This search echnique iniially sars wih LHSP, if minimum BDM poin is found a he cener of LHSP, hen LHSP is shifed o SHSP and new minimum BDM poin found in SHSP is he final moion vecor of he search, oherwise minimum BDM poin found acs as a cener of LHSP and check hree more poins o form a new LHSP, his sep is repeaed unil minimum BDM poin found a he cener of he new generaed large shape paern hexagon and hen lasly shifed LHSP ino SHSP The number of checking poins required for he hexagonal search is 11 for he bes case Search paern of hexagon search algorihm is shown in Fig 14(b) (a) (b) Fig 14 (a) Large Hexagon Search Paern (LHSP) and Small Hexagon Search Paern (SHSP) (b) Search Paern of Hexagon Search 13) Efficien Three Sep Search Xuan Jing e al [94] proposed a modified version of hree- sep search algorihm for block maching moion esimaion, which consiss of unresriced small diamond search paern ha is used o search he cenral area of defined search window and used in wide range of video applicaions like movies, spors ec This algorihm iniially defines a search window size and plos 13 poins on ha search window size In sep 1, ouer 9 poins and small diamond search paern 4 poins (Toal 13 poins) will be checked ie 4 poins more han TSS and 4 poins less han NTSS If he minimum BDM poin is in he cener of search window hen he search will be sopped/hals, oherwise goo sep 2 In sep 2, if minimum BDM poin is one of he ouer 8 poins hen TSS algorihm is used o search he poin, oherwise goo sep 3 In sep 3, If minimum BDM poin is one of he four poins on he small diamond paern, hen consider his poin as a cener and checked anoher 3 poins This process is repeaed unil small diamond search paern Search paern used in firs sep Fig 15 Efficien Three Sep Search 14) New Cross Hexagonal Search Small diamond search paern Kamel Belloulaa e al [88] inroduces a new fas fracal cross hexagonal block maching moion esimaion search algorihm consising of wo cross paern search, ie a Small Cross Shape Paern (SCSP) and Large Cross Shape Paern (LCSP) as a few iniial seps of search, and wo cross hexagon search paern, ie small and large hexagon search paern as a subsequen seps of search as shown in Fig 16, where SCSP: 1 s sep: :, 2 nd sep:, LCSP: 3 rd sep:, LHSP: 4 h sep: and, SHHP: 5 h sep: Final MV This algorihm iniially sars wih small cross shape paern consising of 5 poins locaed a he cener of he search window If minimum BDM poin is found a he cener of he small cross shape paern, hen sop searching ie he number of checking poins required for he new cross hexagonal search is 5 for he bes case, which is beer han all he echniques available for esimaing a moion vecor, oherwise consider a minimum BDM poin as a cener of newly formed small cross shape paern If minimum BDM poin is found a he cener of newly formed small shape paern, hen sop searching ie anoher bes case soluion is 8 checking poins required for finding ou bes possible moion vecor, oherwise check he oher 3 unchecked poins of large cross paern and 2 unchecked poins of he square cener biased o show he bes possible direcion for he hexagonal search A new large hexagonal shape paern is formed by considering a cener poin as minimum BDM poin found in small cross paern search Fig 16: Search Paern in New Hexagon Search If minimum poin is found a he cener of large hexagonal paern, hen large hexagonal paern shifed /changed o small hexagonal paern and find bes moion vecor in small hexagon shape paern, oherwise again form a new large hexagon paern, his formaion of new large hexagon paern is repeaed unil minimum BDM poin is found a he cener of large hexagonal paern

10 Inernaional Journal of Ineracive Mulimedia and Arificial Inelligence, Vol 4, Nº2 15) Modified Three Sep Search S D Kamble e al [95] proposed anoher exended version of hreesep search algorihm consising of Two cross search paern ie small and large cross search paern and wo cross hexagon search paern ie large cross hexagon and small cross hexagon search paern, which are used in he cener par of search window o exploi cenral biased characerisic of MV in video sequences Fig 17 shows he search paern used in modified hree-sep search In he firs sep, oal 9+4 poins are checked ou of 17 checking poins If minimum BDM poin is found a he cener of 9+4 poins, hen sop searching, oherwise go o he second sep If minimum BDM poin is found a he ouer par of he search window, hen search process is same as TSS, oherwise go o he hird sep VII Qualiy Measure Iner-frame and inra-frame coding are used o eliminae a large amoun of emporal and spaial redundancy ha exiss in he video sequences and herefore, help in compressing hem The maching of he one curren frame macro block and previous frame macro block is based on he oupu of maching crieria The macro block ha resuls in he minimum value is he one ha maches he closes o curren block wih respec o he corresponding previous frame macro block The popular maching crieria used for block maching moion esimaion are mean of absolue difference (MAD), mean squared error (MSE) and sum of absolue difference (SAD) given by equaion (8, 9 and 10) respecively MAD (i,j) = (8) MSE (i,j) (9) SAD (i,j) = (10) Search paern used in firs sep Fig 17: Search Paern in Modified Three Sep Search Two Search paern If minimum BDM poin is found a he 4 ouer poins of small cross search paern, hen search process is same as cross hexagon search There is no resricion on searching in he cener window par unless minimum BDM poin is found a he cener of large cross hexagon paern or large cross hexagon search paern reaches o he ouer boundary of he search window This unresriced search in he cenral par of he window increases he probabiliy of finding a rue moion vecor wihin he cener area of he window Some oher approaches are also proposed by Acharjee e al [96-98] Smaller block size based moion esimaion approach for video compression is proposed in [96] Based on he movemen in he video, he low and high moion zone approach exiss in [97], while he scope of parallel processing is experimened in [98] for moion vecor esimaion Generaion of he moion vecor and moion compensaion predicion error plays a key role in video encoding process Therefore, moion vecor dominaes he qualiy of reconsruced frames/video Y -G Wu and G -F Huang [99] proposed moion vecor generaion using gray heory [100] proposed in 1982 Fig 18 shows video compression process flow using moion compensaion WFA encoder where N N is he row and column of he macro block, C ij and R ij are he pixels values compared in he curren macro block and previous macro block, respecively In Block maching algorihms, he size of macro block is he imporan parameer for moion esimaion Smaller macro block size resuls in more moion vecors and more macro blocks per frame Therefore, qualiy of moion compensaed predicion error (MCPE) is improved Mos video coding sandards used a macro block of size and 8 8 The bes/single moion vecor is compued for each macro block in he reference frame On he oher hand, he oal number of search poins o find moion vecor per frame is one of he key parameers in block maching moion esimaion algorihm The performance of video coding is measured in erms of compression raio, qualiy of he video, encoding ime and decoding ime The compression raio is given by equaion (11) Compression raio (CR) = (11) Therefore, he compression raio in percenage is compued from (11) and given by equaion (12) Compression raio (CR) in % = ( ) (12) When measuring he qualiy of he compressed video, he peak signal-o-noise raio is used Someimes mean squared error (mse) is also used, which is given by equaion (13) mse = (13) From his, he PSNR for an 8-bi grayscale image is defined by equaion (14) PSNR(dB) = ) (14) Where 255 is he maximum value an 8-bi pixel can assume VIII Discussion and Conclusion Fig 18 Video Compression Process Flow Based on he sudied lieraure, i is found ha here is a scope of improvemen on he challenging issues of fracal coding like searching bes domain block, domain pool size reducion, pariioning scheme, domain pool classificaion and use of parallel compuing archiecure for fracal compression The major challenge in fracal coding is how o reduce he encoding ime due o a large number of compuaions involved in fracal coding From he sudied lieraure on auomaa

11 Regular Issue heory based exising compression approaches, any image/sequence of image is represened by he finie auomaa The auomaa heory based coding echnique is similar o he fracal coding echnique for searching he self similariy pars presen in he image/sequence of image and regular expression noaion is used o specify he address of each subimage Our observaions from he experimenaion carried ou by conribuors/auhors who have already conribued heir work on auomaa heory based coding approach on he differen exising daabases, i achieves he high compression raio, good image/video reconsrucion qualiy, fas decoding and reducion in encoding ime The exising differen block maching moion esimaion approaches for finding ou he moion vecors in a frame are widely acceped by video compression research communiy/sociey and efficienly used in video compression sandards are- H261 o H263 and MPEG-1 o MPEG-4 Our observaions from he sudied lieraure on differen block maching moion esimaion algorihms are given in Table II Table II shows he performance comparison for differen exising approaches discussed in his paper based on number of search poins per block and Table III shows comparaive performance analysis based on he parameers ie sep size, number of seps required and performance for small/large moions TABLE II Performance Comparison based on Number of Search Poins/ Block Search Bes Wors Search shape paern Average case mehod case case TSS Square DLS Cross + Square OSA Line CSA Cross BS Square o NTSS Square o FSS Square o BBGDS Square o34 34 DS Diamond o CDS Cross + Diamond o HS Hexagonal o ETSS Square + Diamond o <=29 <= 29 CHS Cross + Hexagonal 5/8 5/8 o <36 < 36 MTSS Square + Hexagonal + Cross o <29 < 29 Search mehod TABLE III Comparaive Performance Analysis on Parameers Small moions Large moions Number of seps required TSS No perform beer Perform beer 3 2DLS No perform beer Perform beer 3 OSA No perform beer Perform beer 3 CSA No perform beer Perform beer Condiional BS Perform beer No perform beer Boundary NTSS Perform beer Perform beer 3 FSS No perform beer Perform beer 4 BBGDS Perform beer No perform beer Boundary DS Perform beer No perform beer Condiional CDS Perform beer No perform beer Condiional HS No Perform beer Perform beer Condiional ETSS Perform beer Perform beer 3/ Boundary CHS Perform beer Perform beer Condiional MTSS Perform beer Perform beer 3/Condiional IX Fuure Scope There is a scope of improvemen in developing a new shape paern or modifying exising shape paern approaches by combining wo differen algorihms ie developing a hybrid approach for finding ou he moion vecors for fracal and oher compression echniques wih specific focus on reducing he encoding ime and beer reconsrucion qualiy of video We can exend he block moion esimaion approach in fuure by combing wih he exended version of he finie auomaa for exploring he fracal video compression We can also use hese block maching moion esimaion approaches for racking a single/muliple objec racking 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14 Inernaional Journal of Ineracive Mulimedia and Arificial Inelligence, Vol 4, Nº2 Shailesh D Kamble received Bachelor of Engineering degree in Compuer Technology from Yeshwanrao Chavan College of Enginering, Nagpur, India under he Rashrasan Tukdoji Maharaj Nagpur Universiy, Nagpur, India He received Maser of Engineering degree from Prof Ram Meghe Insiue of Technology and Research, formerly known as College of Engineering, Badnera under San Gadge Baba Amravai Universiy, Amravai, India Presenly, he is PhD candidae in deparmen of Compuer Science and Engineering from GH Raisoni College of Engineering (An Auonomous Insiuion Affiliaed o Rashrasan Tukdoji Maharaj Nagpur Universiy), Nagpur, India He is working as he Assisan Professor in Deparmen of Compuer Science and Engineering a Yeshwanrao Chavan College of Enginering, Nagpur, India He is having over 15 years of eaching and research experience His curren research ineress include image processing, video processing and language processing He is he auhor or co-auhor of more han 20 scienific publicaions in Inernaional Journal, Inernaional Conferences, and Naional Conferences He is he life member of ISTE, India, IE, India Nileshsingh V Thakur received Bachelor of Engineering degree in Compuer Science Engineering from Governmen College of Engineering, Amravai, India and Maser of Engineering degree from College of Engineering, Badnera under Amravai Universiy and San Gadge Baba Amravai Universiy in 1992 and 2005 respecively He received PhD degree in Compuer Science Engineering under Deparmen of Compuer Science Engineering from Visvesvaraya Naional Insiue of Technology, Nagpur, India on 1s February, 2010 His research ineres includes image and video processing, sensor nework, compuer vision, paern recogniion, arificial neural nework and evoluionary approaches He is having over 24 years of eaching and research experience Presenly, he is Professor and Head in Deparmen of Compuer Science and Engineering and Dean, PG Sudies a Prof Ram Meghe College of Engineering and Managemen, Badnera- Amravai, Maharashra, India He is he auhor or co-auhor of more han 70 scienific publicaions in refereed Inernaional Journal, Inernaional Conferences, Naional Journal, and Naional Conferences He is a member of ediorial board of over eigh Inernaional journals; also, he is he life member of ISTE, India, IAENG and IAEME He also worked as he reviewer for refereed inernaional journals and conferences Preei R Bajaj is an Elecronics Engineer Graduaed in 1991, Pos graduae in 1998 and awarded docorae in Elecronics Engg in 2004 Having 25 year of experience, currenly she is Direcor of GH Raisoni College of Engineering, Nagpur Her research ineres includes Inelligen Transporaion Sysem, Sof Compuing, Hybrid Inelligen Sysems & Applicaions of Fuzzy logic in ITS Her professional sociey affiliaion includes Fellow- Insiue of Engineers, Fellow IETE, Senior Member- IEEE, LM-ISTE, and LM- CSI, member ACM She is presenly Vice chair sudens aciviies- India council for IEEE She is he firs Indian o be seleced as Technical Commiee Chair on Sysem Man and Cyberneics Sociey of he IEEE She had been consulan for NHAI, Governmen of India Six Docoral sudens have been awarded PhD & 15 have done masers under her She has auhored & co-auhored 100 plus reviewed Publicaions She has published hree book chapers She is founder General Chair of ICETET series of IEEE Conferences Under her leadership GHRCE has been awarded Auonomy, NBA accrediaion o 21 programs, NAAC wih A Grade, TEQIP World Bank projec 1 and 2 leading o ransform a privae Engineering insiuion ino India s premier, Engineering and Research Insiue