VIDEO COMPLETION USING HIERARCHICAL MOTION ESTIMATION AND COLOR COMPENSATION

VIDEO COMPLETION USING HIERARCHICAL MOTION ESTIMATION AND COLOR COMPENSATION Jn-Hong Km 1, Rae-Hong Park 1 and Jno Lee 1 1 Department of Electronc Engneerng, Sogang Unversty, Seoul, Korea sosu02kjh@sogang.ac.kr, rhpark@sogang.ac.kr, realltybj@sogang.ac.kr ABSTRACT Vdeo completon s used for reparng damaged or mssng data n a vdeo sequence. Ths paper proposes a vdeo completon method usng herarchcal moton estmaton and color compensaton. The proposed method conssts of three steps: herarchcal moton estmaton, source patch search, and color assgnment. If an nput vdeo contans brghtness change, exstng vdeo completon methods gve poor results. For overcomng ths drawback, we apply two methods: color normalzaton for moton estmaton and source patch search, and color compensaton for color assgnment. Frst of all, we construct spato-temporal Gaussan pyramd for reducng the computaton tme. We estmate the moton of the nput vdeo usng color nformaton of each pyramd level. The proposed method uses normalzed RGB color n moton estmaton. Then, we search the source patch usng normalzed color nformaton and moton nformaton extracted n the frst step. Fnally, color compensaton usng color transform s used for color assgnment. Expermental results show that the proposed method gves better vsual results than conventonal vdeo completon methods. KEYWORDS Vdeo completon, moton estmaton, Gaussan pyramd 1. INTRODUCTION Vdeo completon s the method that flls the mssng or damaged regon n a vdeo. It can be regarded as an extenson of mage npantng. Image npantng flls the mssng hole n an mage utlzng spatal nformaton [1] [4]. Image npantng can be used to repar scratch or remove unpleasant objects n photographs. In addton, t can be appled to restoraton of vdeo frame by frame [2]. Nevertheless, mage npantng methods cannot be smply appled to vdeo completon because vdeo has both spatal and temporal nformaton. Vdeo completon methods take account of not only spatal nformaton but also temporal nformaton such as moton to whch the human eye s senstve. Thus, t s mportant how well we utlze both spatal and temporal nformaton for vdeo completon. Shen et al. used moton manfold and volume rectfcaton for vdeo completon [5]. Moton manfold can reduce vdeo search space from three-dmenson to two-dmenson. Volume rectfcaton s used for dealng wth perspectve camera moton. Ther method can deal wth global llumnaton change. Patwardhan et al. extracted the background and movng foreground from a vdeo sequence usng an optcal-flow based mask, after whch the background and foreground are separately flled [6]. It has a drawback that the vdeo sequence for completon should have the statc background, otherwse, t cannot effectvely separate the background and foreground. Ja et al. proposed a vdeo completon method n whch cyclc moton s sampled and ntrnsc mage separaton s used [7]. Ther method extracts llumnaton components from an nput vdeo. After separatng color and llumnaton, color and llumnaton are repared. Ther method can repar the vdeo whch has varable llumnaton such as movng lghtng, however, DOI : 10.5121/jcga.2013.3102 17

extractng correct ntrnsc mages s usually dffcult. For reparng movng foreground, damaged movng elements are extracted and repared by usng sampled cyclc moton. Kamel et al. appled a Kalman flter to a vdeo completon [8]. Kalman flterng and background subtracton are combned to fll foreground. An examplar based mage completon method used for background reparng. Ther method can be applcable to statc nput vdeo. Shh et al. proposed a vdeo completon method for modfyng character s actons [9]. They used skeleton based nterpolaton and examplar-based mage completon for makng new actons. Ther method can create new actons of human character, but t s hard to create actons of other objects. Lee et al. proposed a vdeo completon for mult-vew vdeo sequences [10]. When vdeos have dfferent vew pont for each frame, smple copy and paste methods lke examplar-based completon method do not gve good results. Ther method uses correspondng masks for fndng matchng grd ponts and transformed matchng ponts. Wang et al. used moton states to repar largely occluded movng human [11]. They assumed that perodc human moton can be dvded nto some moton. Usng some categorzed moton, human moton can be repared. Ther method s specalzed to perodc human moton, thus not used for a vdeo that has non-perodc moton. Chang and Shh proposed a method for reparng old moves [12]. Old moves usually have defects. Frstly, these defects are flled wth temporal nformaton. When reparng by temporal nformaton fals, spatal nformaton s used. Ths method uses temporal and spatal nformaton. Shrator et al. used moton vectors (MVs) obtaned by an optcal flow technque [13]. Ths method copes the source MVs that are most smlar to the target MVs. Then, t propagates color from neghborng frames usng flled MVs. Wexler et al. coped pxels from other portons of a vdeo to complete mssng regons [14]. The procedure repeats untl an objectve functon value converges. Ths method works well for statc scenes, whereas t doesn t work for dynamc scenes or n the case where there s no perodc moton. Cheung et al. proposed a vdeo completon method based on patchbased probablty models [15]. They extracted sample patches for unsupervsed learnng. After unsupervsed learnng, vdeo eptomes that represent characterstcs of nput vdeo are constructed. Ths method can be applcable to other felds of tasks such as mage segmentaton and super resoluton, however t does not gve temporal smoothness of the result vdeo well. Fang and Tsa proposed a vdeo completon method for removng advertsement vdeo texts [16]. Ths method uses a herarchcal model for flng targeted regons. Temporal structure and texture are repared at the top of the herarchcal model. At the bottom of the herarchcal model, spatal texture regon s flled by gradent dervatve propagaton and duplcaton. Ths method consders structure and texture smultaneously, but gradent dervatve propagaton makes texture blurry. Dng et al. proposed a vdeo completon method usng rank mnmzaton [17]. Feature descrptors are obtaned by rank mnmzaton. Feature descrptors are used for restorng vdeo frames. Ths method can gve good results for statc and non-statc vdeo, and can handle wth non-perodc object moton. Chang et al. combned moton compensaton and examplar mage completon method [18]. Ther method can repar both statc and non-statc vdeo. If an nput vdeo has no temporal contnuaton, ther method shows poor completon results. Ths paper s an extended verson of the paper [19], n whch performance comparson of three exstng methods s presented for three test mages. The rest of the paper s organzed as follows. Secton 2 descrbes n detal the proposed algorthm. Frst, we explan color optcal flow usng spato-temporal Gaussan pyramd. After fndng source patch step s descrbed, the source patch s searched herarchcally. Fnally, fllng color nformaton usng color compensaton s descrbed. Secton 3 gves expermental results and dscussons. Fnally, Secton 4 concludes the paper. 2. PROPOSED VIDEO COMPLETION METHOD USING COLOR COMPENSATION In ths secton, we descrbe the proposed vdeo completon method usng color compensaton for a vdeo that has brghtness change. Fgure 1 shows the block dagram of the proposed method. Frst, we estmate moton of the nput color vdeo I usng color nformaton. Then, we search 18

space-tme source patches to fll the target regon H. Fnally, damaged regon s flled wth color compensated source patch. Fgure 1. Block dagram of the proposed vdeo completon algorthm 2.1. Herarchcal moton estmaton usng color nformaton Temporal nformaton s used to fnd source patches. We assume that moton nformaton s contnuous across the nput vdeo. We assgn moton nformaton that mantans consstency of moton usng the source patches to fll the hole effectvely. Exstng ntensty-based moton estmaton approach extracts naccurate moton nformaton, because of varaton of brghtness between patches. Golland and Brucksten proposed two methods under dfferent assumptons: brghtness conservaton and color conservaton [20]. Wth two assumptons, we can explan the dsadvantage of ntensty based moton estmaton. Brghtness conservaton model assumes that there s no brghtness change of three color channels between the current and prevous frames. Thus, moton nformaton of the current frame can be estmated ntensty change of three channels, whch s express as R R R u + v + = 0, G G G u + v + = 0, B B B u + v + = 0 (1) (2) (3) where R, G, and B represent red, green, and blue color components of a color mage, u and v are x and y drectonal motons, respectvely. Under the brghtness change, ntensty varaton of each channel affects the accuracy of moton estmaton. Moton estmaton under color conservaton assumpton conssts of two equatons that use color components, whch are expressed as F F F 1 1 1 u + v + = 0, F2 F2 F2 u + v + = 0 (5) where F 1 and F 2 are color components, and u and v are motons along the x and y drectons, respectvely. Normalzed RGB color s used to remove brghtness component and to extract more accurate moton nformaton, whch s represented as (4) 19

R r R + G + B G g =. + + b R G B B R + G + B (6) The normalzed RGB represents RGB color components that do not have brghtness component. In other words, (6) removes brghtness component from RGB color. The normalzed RGB color helps extract moton nformaton more accurately than RGB color when an nput vdeo has brghtness change [12]. Note that F 1 s r and F 2 s g of I n the proposed method, and (4) and (5) are rewrtten as r1 r r u + v + = 0, g g g u + v + = 0. (7) (8) Before estmatng moton, we construct l-level spato-temporal Gaussan pyramd to reduce the computaton tme. A vdeo at coarser level has half resoluton of a vdeo at next fner level. Moton estmaton s done from coarse to fne level usng (7) and (8). 2.2. Space-tme source patch search Damaged regon s flled by other porton of an nput vdeo usng a patch-based fllng process. All patches n the target regon are compared wth patches n the source regon and the best matched sample of each target patch s found. We search a mnmum dfference patch for the patch matchng. The proposed method uses normalzed RGB color for accurately fndng source patches to correct color dfference between target and source regons. We employ the objectve functon for searchng space-tme source patch [14]. A completon result Î can be obtaned by maxmzng the objectve functon teratvely, whch s defned as Coherence( Iˆ D) = p Iˆ maxsm( W, V ) q D (9) where D s a database vdeo that represents portons of I outsde of the target regon H. Whole vdeo sequence except for target completon regon s used as a database vdeo. Fgure 2 shows a relaton between a target patch W and a source patch V between database D and target regon H. Space-tme pxels p and q are denoted by three-dmensonal (3-D) vector (x,y,t) T n each porton of Î and D, respectvely. W s a space-tme target patch that ncludes p, whereas V s a spacetme source patch that ncludes q. The sze of a space-tme patch s defned as (2S+1) (2S+1) (2S+1). When the objectve functon converges, pxels n the target regon can be regarded as correctly repared. 20

Fgure 2. Source patch V and target patch W n (2). A global objectve functon defnes global coherence between Î and D as a local smlarty between space-tme patches. An mportant thng n defnng a global objectve functon s how to defne a local smlarty measure. Image completon usually uses the sum of squared dfference (SSD) of color components for a smlarty measure. It s not suffcent to use color components alone for a smlarty measure n vdeo completon. Because human eyes are more senstve to moton than to color nformaton, moton nformaton s added for measurng local smlarty [14]. Consderng ths characterstcs of human eyes, the local smlarty between space-tme patches s expressed as sm ( W, V ) d exp 2 ( W, V ) = 2 (10) where d(, ) s the SSD of the fve-dmensonal (5-D) feature vector between space-tme patches, whch s expressed as d ( W V ), = W s W H, V s L s W ( x, y, t) s V ( x, y, t) 2 (11) where L s a set of space-tme pxels n V. The feature vector s s defned as s ( r, g, b, u, v) T (12) where r, g and b are the normalzed R, G, and B components, u and v are x and y drectonal motons, respectvely, and λ s a scale factor. For reducng the overall error, s set to 75 percent of all dstances n current search. Usng R, G, and B components for a 5-D feature vector drectly, an optmal space-tme source patch s not found correctly. Snce the normalzed RGB color does not contan brghtness component, the local smlarty usng the normalzed RGB color model can accurately measure the smlarty between space-tme patches. Usng spato-temporal Gaussan pyramd, the computaton tme of searchng source patch can be reduced. After determnng the poston of source patches at the coarser level, the poston of source patches s found at the fner level usng the coarse poston of source patches. 21

2.3. Color compensaton and color assgnment Brghtness changes over the captured mages n the same scene wth varyng vewponts may create large color artfact when they are merged [21] [23]. For color constancy of mergng mage, color dfference of overlapped regons must be corrected. Smlarly, wthout the consderaton of the dfference of brghtness between two patches, color nformaton cannot be used drectly for color assgnment. Assgnng colors n the source frame to the target frame wthout color correcton process ntroduces color dstorton snce color vares over the nput vdeo sequence. Hence, we compensate for color nformaton of source patches usng color transform functon. In the proposed method, we use the affne transform model [21]. The transform matrx M between target frame and source frame s defned as M = T 1 T ( Q Q) Q P (13) where P and Q are N 3 RGB color matrces that are represented as R( p1) G( p1) B( p1) P = R( p ) ( ) ( ) N G p N B p N R( q1) G( q1) B( q1) Q =. R( q ) ( ) ( ) N G q N B q N (14) (15) P and Q contan space-tme pxels p and q, respectvely. N s the number of pxels n the spacetme patches. The offset vector of the affne transform s obtaned usng the mean vectors of pxels n patches m P and m Q, respectvely, whch are represented as m m p q = = P P P [ ] T R G B Q Q Q [ ] T R G B (16) (17) P P R G Q Q G where,, and Q B P B are mean values of R, G, and B values n the target patch, respectvely, and R,, and are mean values of R, G, and B values n the source patch, respectvely. In ths way, the transform of color c at each space-tme pxel can be represented as c ( p) = c( q) M + m Mm. P Q (18) Color values n whole patches ncludng p are corrected. Fnally, color c at p s assgned as ĉ = w p c w p (19) where sm( w p s the relablty of th target patch contanng p, whch can be measured usng (10) as, V ), wth the source patch most smlar to th target patch. W p 22

3. EXPERIMENTAL RESULTS AND DISCUSSIONS Expermental results show the comparson of vdeo completon performance of the proposed method and three conventonal methods: Patwardhan et al. s method [6], Shrator et al. s method [13], and Wexler et al. s method [14]. In Fgure 3, we use the Walkng Woman vdeo sequence (320 240) wth arbtrary brghtness change. Fgure 3(a) shows example mages of syntheszed nput vdeo that has brghtness change. The next frame of the nput vdeo s generated, wth 10 percent brghter than the prevous frame. A damaged frame s created by rectangular mask (50 70). Fgures 3(b), 3(c), and 3(d) show enlarged result frames of Patwardhan et al. s method, Shrator et al. s method, and Wexler et al. s method, respectvely. The error propagaton can be found n result mages of conventonal methods. Artfacts are propagated from boundary to nsde of the target regon n Fgures 3(b) and 3(d). These methods fal to reconstruct the shape of a woman s body. Shrator et al. s method gves better result than other conventonal methods, as shown n Fgure 3(c). However, artfacts are also founded n the result mage. Ther method cannot assgn proper color nformaton to the target regon. In Fgure 3(e), on the contrary, the result mage of the proposed method shows plausble vsual qualty. (a) (b) (c) (d) (e) Fgure 3. Comparson of result mages (320 240, Walkng Woman ). (a) nput vdeo, (b) enlarged result frame by [6], (c) enlarged result frame by [13], (d) enlarged result frame by [14], (e) enlarged result frame by the proposed method. Fgure 4 shows the expermental result usng a Char vdeo sequence (320 240) used n Ja et al. s experment [7]. Fgure 4(a) shows example mages of nput vdeo that has brghtness change. Fgures 4(b), 4(c), and 4(d) show enlarged result frames of Patwardhan et al. s method, Shrator et al. s method, and Wexler et al. s method, respectvely. A result of Patwardhan et al. s method shows boundary dstorton. It fals to reconstruct shape of the char at an ntersecton pont of two edges. Though, the performance of Patwardhan et al. s method s better than that of Shrator et al. s and Wexler et al. s methods n flat regon. Shrator et al. s results show well reconstructed 23

contour. However, there are artfacts n the flat regon. In Fgure 4(c), unnatural edges are found at top of a char. Wexler et al. s result shows better result than Patwardhan et al. s and Shrator et al. s methods. However, Fgure 4(d) shows regons flled wth unnatural colors. The result mage of the proposed method shows plausble vsual qualty as shown n Fgure 4(e). It shows sharper edges and more unform colors than other methods n the completon regon. (a) (b) (c) (d) (e) Fgure 4. Comparson of result mages (320 240, Char). (a) nput vdeo, (b) enlarged result frame by [6], (c) enlarged result frame by [13], (d) enlarged result frame by [14], (e) enlarged result frame by the proposed method. Fgure 5 shows the real Door vdeo sequence (600 317) wth arbtrary brghtness change. Fgure 5(a) shows three frames of the nput vdeo, n whch the damaged regon to be completed s ndcated by the 50 40 rectangle. Fgures 5(b), 5(c), 5(d), and 5(e) show enlarged frames reconstructed by Patwardhan et al. s method, Shrator et al. s method, Wexler et al. s method and the proposed method, respectvely. The conventonal methods do not gve sgnfcant color artfacts compared wth other test vdeos, because vdeos consst of frames wth relatvely neutral color such as gray. The conventonal methods show cloth boundares not well reconstructed whereas the proposed method gves well reconstructed cloth boundares. Result mages of conventonal methods show the poor performance under brghtness change. However, snce the proposed method s based on Wexler et al. s method, the computatonal complexty s almost the same as thers and the proposed method s applcable to the vdeo sequence wth perodc moton. Table 1 shows performance comparson of four mage completon methods n terms of the SSD. The proposed method not only reconstructs smoothly mages but also gves the lower SSD values than the conventonal methods. (a) 24

(b) (c) (d) (e) Fgure 5. Comparson of result mages (600 317, Door ). (a) nput vdeo, (b) enlarged result frame by [6], (c) enlarged result frame by [13], (d) enlarged result frame by [14], (e) enlarged result frame by the proposed method. Table 1. Performance comparson of four completon methods n term of the SSD. Wexler et al. s Shrator et al. s Patwardhan et Proposed method method method al. s method Walkng Woman 3.62 10 3.84 10 3.56 10 0.86 10 Char 8.11 10 8.96 10 8.39 10 5.32 10 Door 3.75 10 3.84 10 3.61 10 1.62 10 4. Concluson In ths paper, we propose an mproved vdeo completon method usng herarchcal moton estmaton and color compensaton under brghtness change. To extract more accurate moton nformaton, color moton estmaton wth normalzed color nformaton s used. For better smlarty measure, we use normalzed color nformaton and herarchcal moton nformaton. Because the color nformaton of each frame s dfferent, color compensaton usng the affne transform s used for color assgnment. Expermental results show the effectveness of the proposed method under brghtness change. REFERENCES [1] A. Crmns, P. Perez, and K. Toyama, Regon fllng and object removal by exemplar-based mage npantng, IEEE Trans. Image Processng, vol. 13, no. 9, pp. 1200 1212, Sept. 2004. [2] M. Bertalmo, L. Vese, G. Sapro, and S. Osher, Smultaneous structure and texture mage npantng, IEEE Trans. Image Processng, vol. 12, no. 8, pp. 882 889, Aug. 2003. [3] A. Levn, A. Zomet, and Y. Wess, Learnng how to npant from global mage statstcs, n Proc. Int. Conf. Computer Vson, vol. 1, pp. 305 312, Nce, France, Oct. 2003. [4] S. Yoo and R.-H. Park, Red-eye detecton and correcton usng npantng n dgtal photographs, IEEE Trans. Consumer Electroncs, vol. 55, no. 3, pp. 1006 1014, Aug. 2009. [5] Y. Shen, F. Lu, X. Cao, and H. Foroosh, Vdeo completon for perspectve camera under constraned moton, n Proc. Int. Conf. Pattern Recognton, vol. 3, pp. 63 66, Hong Kong, Chna, Aug. 2006. [6] K. A. Patwardhan, G. Sapro, and M. Bertalmo, Vdeo npantng under constraned camera moton, IEEE Trans. Image Processng, vol. 16, no. 2, pp. 545 553, Feb. 2007. 25

[7] J. Ja, Y.-W. Ta, T.-P. Wu, and C.-K. Tang, Vdeo reparng under varable llumnaton usng cyclc motons, IEEE Trans. Pattern Analyss and Machne Intellgence, vol. 28, no. 5, pp. 832 839, Mar. 2006. [8] S. Kamel, H. Ebrahmnezhad, and A. Ebrahm, Movng object removal n vdeo sequence and background restoraton usng Kalman flter, n Proc. Int. Symp. Telecommuncatons, Tehran, Iran, pp. 580 585, Aug. 2008. [9] T. K. Shh, N. C. Tan, J. C. Tsa, and H.-Y. Zhong, Vdeo falsfyng by moton nterpolaton and npantng, n Proc. Computer Vson and Pattern Recognton, 10.1109/CVPR.2008.4587701, Anchorage, Alaska, Jun. 2008. [10] S.-Y. Lee, J.-H. Heu, C.-S. Km, and S.-U. Lee, An object npantng algorthm for mult-vew vdeo sequences, n Proc. Int. Conf. Image Processng, pp. 533 536, San Dego, CA, Oct. 2008. [11] H. Wang, H. L, and B. L, Vdeo npantng for largely occluded movng human, n Proc. Int. Conf. Multmeda Expo, pp. 1719 1722, Bejng, Chna, Jul. 2007. [12] R.-C. Chang and T. K. Shh, Spatal-temporal method for mage restoraton n aged moton pctures, n Proc. Int. Conf. Advanced Informaton Networkng and Applcatons, pp. 724 729, Washngton, DC, May 2007. [13] T. Shrator, Y. Matsushta, X. Tang, and S. B. Kang, Vdeo completon by moton feld transfer, n Proc. Computer Vson and Pattern Recognton, vol. 1, pp. 411 418, New York, Jun. 2006. [14] Y. Wexler, E. Shechtman, and M. Iran, Space-tme vdeo completon, IEEE Trans. Pattern Analyss and Machne Intellgence, vol. 29, no. 3, pp. 463 476, Mar. 2007. [15] V. Cheung, B. J. Frey, and N. Jojc, Vdeo eptomes, Internatonal Journal of Computer Vson, vol. 76, no. 2, pp. 141 152, Feb. 2008. [16] C.-L. Fang and T.-H. Tsa, Advertsement vdeo completon usng herarchcal model, n Proc. Int. Conf. Multmeda Expo, pp. 1557 1560, Hannover, Germany, Jun. 2008. [17] T. Dng, M. Sznaer, and O. I. Camps, A rank mnmzaton approach to vdeo npantng, n Proc. Int. Conf. Computer Vson, 10.1109/ICCV.2007.4408932, Ro de Janero, Brazl, Oct. 2007. [18] R.-C. Chang, N. C. Tang, and C. C. Chao, Applcaton of npantng technology to vdeo restoraton, n Proc. Int. Conf. Ub-Meda Computng, pp. 359 364, Lanzhou, Chna, Jul. 2008. [19] J.-H. Km, R.-H. Park, and J. Lee, Vdeo completon usng robust moton estmaton and color compensaton, n 2011 Dgest of Techncal Papers Int. Conf. Consumer Electroncs, pp. 535 536, Las Vegas, NV, Jan. 2011. [20] P. Golland and A. M. Brucksten, Moton from color, Computer Vson and Image Understandng, vol. 68, no. 3, pp. 346 362, Dec. 1997. [21] G. Y. Tan, D. Gledhll, D. Taylor, and D. Clarke, Colour correcton for panoramc magng, n Proc. Int. Conf. Informaton Vsualsaton, pp. 483 488, London, UK, Jul. 2002. [22] Y. Xong and K. Pull, Fast panorama sttchng for hgh-qualty panoramc mages on moble phones, IEEE Trans. Consumer Electroncs, vol. 56, no. 2, pp. 298 306, May 2010. [23] S.-J. Ha, H.-I. Koo, S. H. Lee, N.-I. Cho, and S.-K. Km, Panorama mosac optmzaton for moble camera systems, IEEE Trans. Consumer Electroncs, vol. 53, no. 4, pp. 1217 1225, Nov. 2007. 26

Authors Internatonal Journal of Computer Graphcs & Anmaton (IJCGA) Vol.3, No.1, January 2013 Jn-Hong Km receved the B.S. degree n electronc engneerng from Sogang Unversty n 2004. Currently he s workng toward the M.S. degree n electronc engneerng from Sogang Unversty. Hs current research nterests are mage npantng and mage enhancement. Rae-Hong Park was born n Seoul, Korea, n 1954. He receved the B.S. and M.S. degrees n electroncs engneerng from Seoul Natonal Unversty, Seoul, Korea, n 1976 and 1979, respectvely, and the M.S. and Ph.D. degrees n electrcal engneerng from Stanford Unversty, Stanford, CA, n 1981 and 1984, respectvely. In 1984, he joned the faculty of the Department of Electronc Engneerng, School of Engneerng, Sogang Unversty, Seoul, Korea, where he s currently a Professor. In 1990, he spent hs sabbatcal year as a Vstng Assocate Professor wth the Computer Vson Laboratory, Center for Automaton Research, Unversty of Maryland at College Park. In 2001 and 2004, he spent sabbatcal semesters at Dgtal Meda Research and Development Center, Samsung Electroncs Co., Ltd. (DTV mage/vdeo enhancement). Hs current research nterests are vdeo communcaton, computer vson, and pattern recognton. He served as Edtor for the Korea Insttute of Telematcs and Electroncs (KITE) Journal of Electroncs Engneerng from 1995 to 1996. Dr. Park was the recpent of a 1990 Post-Doctoral Fellowshp presented by the Korea Scence and Engneerng Foundaton (KOSEF), the 1987 Academc Award presented by the KITE, and the 2000 Haedong Paper Award presented by the Insttute of Electroncs Engneers of Korea (IEEK), the 1997 Frst Sogang Academc Award, and the 1999 Professor Achevement Excellence Award presented by Sogang Unversty. Jno Lee receved the B.S. degree n electronc engneerng from Sogang Unversty n 2010. Currently he s workng toward the M.S. degree n electronc engneerng from Sogang Unversty. Hs current research nterests are mage npantng and mage enhancement 27