Fast CU Partition Strategy for HEVC Intra-Frame Coding Using Learning Approach via Random Forests

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1 Fast CU Partton Strategy for HEVC Intra-Frame Codng Usng Learnng Approach va Random Forests Bochuan Du, Wan-Ch Su and Xuefe Yang Centre of multmeda sgnal processng, Department of Electronc and Informaton Engneerng The Hong Kong Polytechnc Unversty, Hong Kong E-mal: Tel: Abstract HEVC (Hgh Effcency Vdeo Codng) acheves cuttng edge encodng effcency and outperforms prevous standards, such as the H.264/AVC. One of the key contrbutons to the mprovement s the ntra-frame codng that employs abundant codng unt (CU) szes. However fndng the optmal CU sze s computatonally expensve. To allevate the ntra encodng complexty and facltate the real-tme mplementaton, we use a machne learnng technque: the random forests, for tranng. Based on off-lne tranng, we propose usng the forest classfer to skp or termnate the current CU depth level. In addton, neghborng CU sze decsons are utlzed to determne the current depth range. Expermental results show that our proposed algorthm can acheve 48.31% tme reducton, wth 0.80% ncrease n the Bjøntegaard delta btrate (), whch are state-of-the-art results compared wth all algorthms n the lterature. I. INTRODUCTION In 2013, the Jont Collaboratve Team on Vdeo Codng (JCT-VC) has come up wth a mlestone of vdeo compresson standard, known as Hgh Effcency Vdeo Codng (HEVC) [1], whch s an evolved verson of H.264 [2]. HEVC outperforms H.264 by ncreasng the compresson rate by about 50% and obtanng a hgh encodng effcency for resoluton beyond Hgh Defnton (HD) vdeos, such as the latest 4K and 8K formats vdeos[3][4]. Compared wth H.264, HEVC has two man enhancements n encoder, ) nested quad-tree structures wth codng unt (CU), predcton unt (PU), and transform unt (TU) [5] and ) numerous predcton modes [4]. One of the consderable areas n HEVC s ntra codng, for whch blocks of pxels are coded only wth references to the current frame. It explots the spatal correlaton on blocks wth 35 predcton modes and CU szes varyng from block sze 64x64 down to 8x8 [6]. These new features affect the complexty extremely, makng t dffcult for real-tme mplementaton, especally for HD or Ultra HD vdeos that have hgh resolutons. The fact motvates researches on early decson of CU sze to reduce the codng complexty and acheve a fast encodng speed. Authors n [7] proposed an onlne updated statstcal parameter to set the threshold for early decson on CU sze splttng and prunng. In [8], texture measurements on each CU block are utlzed to bypass and termnate CU sze decson. Edge complexty n several drectons are used to decde the parttonng of a CU n [10]. However, most of these exstng algorthms manly apply conventonal contentbased or statstc-based methods. Lmted number of papers n the lterature consder to used learnng-based algorthms, whch s currently a hotspot and wdely appled n recognton, classfcaton and sgnal processng. One of the latest lterature that uses learnng-based method on CU sze decson n ntra frame predcton s [9], where data mnng classfers are used. But the tme savng rato mght be far from the stateof-the-art results and the testng sequences used n the paper appear ncomplete for a full evaluaton. Many papers n lterature that explore fast ntra codng algorthms va predcton mode selecton [11][12], whch s also a hot topc for ntra predcton research, whle solely applyng fast predcton mode selecton usually cannot gve an effectve complexty reducton n HEVC. In ths paper, we propose an effcent machne learnng algorthm based on fast CU depth decson, where we manly apply random forests for the learnng. For the rest of ths paper, we wll gve a bref ntroducton of HEVC ntra-frame predcton n secton II, manly focusng on CU sze decson. Secton III covers the detals of our proposed algorthm, whch ncludes CU range decson and the fast splt or nonsplt strategy. In secton IV, we wll show and compare expermental results. Fnally, the concluson and references are provded. II. INTRA PREDICTION IN HEVC Intra predcton s a block-based predcton. Compared wth the H.264 standard where three block szes wth four (for 16x16 block sze) and nne (for 8x8 and 4x4 block sze) predcton modes are avalable to predct a block wthn the current frame [1], the HEVC not only enlarges the block szes to 64x64, 32x32, 16x16, 8x8 and 4x4, but also ncreases the set of predcton modes to 35 [2]. HEVC starts the predcton from a Codng Tree Unt (CTU) whose sze s 64x64. It wll then be further splt nto four 32x32 CUs, each of the four sub-cus can also be splt teratvely nto a smaller quad-tree-based structure [6]. Fgure 1 gves an example of the CTU sze decson on both the tree structure and correspondng block parttons. Depth 0 refers to the block sze 64x64. And the number 0 and 1 represent the APSIPA 1085 APSIPA ASC 2015

2 non-splt and splt decson. If the cost of predcton on the four 32x32 blocks s lower than the cost of predctng 64x64 block, the depth level wll be ncreased by one and goes nto the analyss of each 32x32 blocks. Smlarly, a 32x32 CU wll be compared wth ts four 16x16 sub-cus. When t comes to 8x8 CU, t may be dvded nto PUs wth sze of 4x4. Fnally, the parttonng ends untl the mnmum cost n each level s found. and we have M=4, ncludng the left CU, left-above CU, above CU and rght-above CU. w s the weghtng factor determned by the correlaton between the current CU and each neghborng CU. The sum of these four weghts s normalzed to 1. Due to a stronger correlaton, we assgn w =0.3 for the left CU and above CU. For the left-above CU and rght-above CU whch have weaker correlaton wth the current CU, we let w =0.2. Ths arrangement s for the sake of smplcty. After obtanng the Depth pred the current CU depth range s assgned as below. (2) Fg. 1 An example of CU parttons In each CU, 35 predcton modes, ncludng 2 modes for smooth texture predcton and 33 angular predcton modes, are compared by Rough Mode Decson (RMD) [13][14][15] and Rate Dstorton Optmzaton (RDO) [6]. Then the optmum mode wth the lowest cost s obtaned. Due to numerous amount of possbltes to be evaluated, HEVC s tme-consumng. Ths fact evokes our approach to realze an early CU sze decson wthout gong through the tree structure. III. OUR PROPOSED FAST CU DECISION A. CU Depth Range Decson In a natural scene, neghborng blocks have a strong correlaton wth the current CU. It s known that the ntraframe codng makes use of the encoded above, left, left-above and rght-above CUs as shown n Fgure 2 to reduce computatonal burden n the current CU. Fg.2 Current CU and ts Neghbor Depth Decson These neghborng CUs usually have smlar contents, but HEVC yet has no good methods to ad the current CU sze decson by utlzng ts neghbors. We can make use of the spatal correlaton of them, and predct a depth level as below. M 1 0 Depth w Depth (1) pred where Depth s the depth level of the neghborng CUs, rangng from 0 to 3. M s the number of neghborng blocks, When the predcted depth s smaller than 1, t means the neghborng blocks are flat and homogeneous. The current depth range can be set from 0 to 2, where the smallest CU sze for predcton s lmted to 16x16 (depth 2). When the predcted level s larger than 2, ths reveals that the neghborng CUs are complex and nhomogeneous. We wll set the predcton range from 1 to 3, whch means the current CU predcton starts from block sze 32x32 (depth 1), and searches towards 8x8 (depth 3). For the rest cases of the predcted depth, the predcton range remans unchanged, whch s the same as the default HEVC settngs. Ths approach s smlar to reference [8] and [16], except that [8] s lmted to bypass the current block sze when the current depth level s smaller than the predcted depth mnus one. And [16] defnes the depth range by consderng the ranges of the left and above CU only. B. Early CU Termnaton and Bypass Decson by Random Forests In ths secton, we ntroduce the core of our proposed algorthm. Frst, our forest desgn s gven wth a bref ntroducton of decson tree and random forests. Then a splt algorthm s ntroduced for the optmum splt decson. Lastly, we wll provde detaled strateges for the tranng, and the evaluatons of the tranng results. 1) Forest Desgn The approach random forests [17]-[20], T={T t}, s a combnaton of a set of decson trees {T t} whch jontly realze classfcaton. Each tree has three types of nodes: A root node that has no ncomng edges wth zero or more outgong edges. Internal nodes, each of whch has exactly one ncomng edge and two or more outgong edges. Leaf or Termnal nodes, each of whch has exactly one ncomng edge and no outgong edges. The predcton starts wth the root node. For each non-leaf node, whch ncludes the root and nternal nodes, the procedure allows an ncomng mage block to go to the left or to the rght based on the value of a certan varable whose ndex s stored n the current node. The tree s bult recursvely, at each node the recursve procedure may stop when: APSIPA 1086 APSIPA ASC 2015

3 Depth of the tree branch has reached the maxmum defned value. Number of tranng samples s less than a specfed threshold when further splt s not statstcally representatve. All the samples belong to the same class. In our desgned forests, all the trees are traned wth dfferent tranng sets but wth the same set of parameters. These sets are generated from the orgnal tranng set usng the bootstrap procedure. We totally uses 10 trees, where each of them has a maxmum depths of 25. We apply the luma pxel ntensty n each block as the nput feature vector, and the output classfcaton s 0 for non-splt decson and 1 for splt decson. As shown n Fgure 3, the classfcaton works as below: each random tree classfer takes the nput mage block, classfy t by gong through the decson tree, and outputs the class label 1 or 0 for each tree. The fnal decson s made at the classfcaton result that receved the majorty of votes from all the 10 outputs. Fg. 3 Random forest wth a set of decson trees. 2) Splt Algorthm In the tranng of each non-leaf node, a set of thresholds s tested. If the pxel value s less than the threshold, the mage block goes to the left, otherwse t goes to the rght. The optmum threshold can be obtaned by many measures. In our experments, we use the Gn mpurty crteron [19] c 1 Gn ( t) 1 [ p( t)] (3) 0 where c s the number of classes (here we have c=2), p( t) denotes the rato of data belongng to class at the node t. If the Gn mpurty s lower, the classfcaton performance s better. Then, by gong through all possble thresholds, the optmum one s obtaned wth the lowest Gn mpurty. For example, there are two thresholds Th 1, Th 2 need to be tested. Threshold Th 1 results n 10 tranng samples gong to the left node, where 8 of them are classfed as 0, and the rest 2 of them are classfed as 1. The Gn mpurty for Th 1 s Gn Th1 1 ( ) ( ) 0.32 (4) Threshold Th 2 results n 20 tranng samples gong to the left node, where 10 of them are classfed as 0, the other 10 samples are classfed as 1. The Gn mpurty for Th 2 s Gn Th2 1 ( ) ( ) 0.50 (5) Snce Gn Th1 < Gn Th2, Threshold Th 1 s then selected as the optmum threshold for the splt. 3) Tranng Scheme and Evaluaton Feature selecton ams to select attrbutes that can realze a hgh classfcaton performance. In our proposed method, we take the luma pxel ntensty as the nput attrbutes for the CU sze 64x64 and 32x32 determnaton. The classfcaton result s set to 0 whch means to stay n the current depth wthout any further splt and 1 as to skp the current CU depth and start the predcton from one depth hgher. As for a block belongs to 0, t has a homogenous texture or a smooth gran that follows a certan drecton. In ths way, the block can be easly predcted as one of the 35 predcton modes and results n tny resdual and RD cost. On the other hand, for the block belongs to 1, t owns a vared texture and further splt s requred. The detaled parameters for our tranng s as below. TABLE I TRAINING PARAMETER SETTINGS FOR BLOCK DEPTH 0 AND 1 Parameters Numbers Number of tranng samples 2400 for block depth for block depth 1 Attrbutes per sample (block sze) Number of Classfcaton Max depth 25 Mnmum number of tranng 5 sample for further splt Number of varable randomly selected at node and used to fnd the best splts Number of trees n the forest (=64x64) for block depth 0 2 ( 0 for non-splt, 1 for splt) 64 for block depth 0 32 for block depth 1 The number of tranng samples s set to the extent of a hgh predcton accuracy wthout overfttng. The number of attrbutes per sample, whch means the number of nput feature vectors per sample, s 4096 for mage block sze 64x64 and 1024 for mage block sze 32x32. Then the classfcaton decson s assgned as the last number n a sample, where 0 s for non-splt decson and 1 for splt decson. The maxmum depth of a decson tree s set to 25, whch s obtaned by experments from settng the maxmum depth as 5, 10, 15, The mnmum number of tranng samples for further splt s set to 5. And the number of varables randomly selected at node and used to fnd the best splt s 64 for block sze 64x64, and 32 for block sze 32x32, whch s useful to explore some texture characterstcs. Fnally, we set 10 as the number of trees n the forest. Consderng the optmal parttonng obtaned based on pxel varaton n the ntra-frame predcton has strong dependency wth the scene and the QP value, we traned the classfer sequence by sequence wth four QP values: 22, 27, 32, and 37 as defned n JCT-VC [21]. The ground truth classfcaton s obtaned by encodng the HEVC testng model HM10.0 [22]. Table II and Table III show part of our results about the predcton accuracy on the frst 200 frames s of Class B sequences. From the tables above, we can fnd that APSIPA 1087 APSIPA ASC 2015

4 ths method acheves a hgh predcton accuracy, where most of the results can reach an accuracy of over 90%. The results can guarantee a fast and effcent depth decsons. For the blocks wth wrong classfcaton, there mght be some degradaton n PSNR and ncrease n. But even the sze of CU s wrongly predcted, for example, the ground truth s to stay at depth 0. Our decson stops at depth 2 as shown n Fgure 4. The qualty may not be affected severely because further splt can also acheve an accurate predcton. Wth the low wrong predcton rate, the degradaton of pcture qualty should be low. TABLE II PREDICTION ACCURACY OF CU DEPTH 0 OF CLASS B CU Depth 0 Predcton Average Testng Sequence QP Tranng Basketball BQTerrace Cactus Kmono Park Tme (s) Fg.5 Flowchat of the proposed algorthm confguraton of All-Intra Mode and Man profle (AI-Man). Four QP values (QP 22, QP 27, QP 32, and QP 37) were used to encode all the frames n the test sequences. For each QP of a sequence, we off-lne traned two forests n advance, whch follows our methodology ntroduced n secton The Bjøntegaard delta rate dstorton () performance s measured by Model [23] [24]. The average tme rato () and average tme savng (ATS) are gven by: TABLE III PREDICTION ACCURACY OF CU DEPTH 1 OF CLASS B CU Depth 0 Predcton Average Testng Sequence QP Tranng Basketball BQTerrace Cactus Kmono Park Tme (s) n Enc. Tmeproposed, n Enc. Tme 1 HM10.0, (6) 1 ATS n n Enc. Tme HM10.0, Enc. Tme Enc. Tme 1 HM10.0, proposed, (7) Fg.4 Comparson on CU Sze Decson. (Left: ground truth CU sze encoded by HM10.0. Rght: predctons of our algorthm, all matchng well, except the CU encoded n red.) C. Flowchart of our Proposed Algorthm Fgure 5 sums up our proposed algorthms as llustrated n secton III.A and III.B. When CU encodng starts, the depth range decson wll frstly be found by makng use of neghborng blocks. Then, f the depth level of the current CU s 0 or 1, we use random forests classfers to decde whether the predcton should be bypassed or stayed on the current depth level. For the rest of CU depths (level 2 and 3), conventonal HEVC ntra predcton methods wll be used. IV. EXPERIMENTAL RESULT The proposed fast algorthm s mplemented on the HEVC reference software HM10.0 reference software [22], and was run on a platform wth Intel Core TM CPU 16.0GB RAM sze. Accordng to the common test condtons and reference confguratons recommended by JCT-VC [21], fve groups of testng sequences were carred out to evaluate the codng performance and computatonal complexty under the where n s the total number of sequences, Enc.Tme proposed, s the encodng tme of our proposed algorthm on sequence, and Enc.Tme HM10.0, s the correspondng encodng tme of HM10.0. Table IV summarzes the performance of the overall proposed algorthm. As shown n the tables, the proposed algorthm effcently reduces the computatonal complexty n terms of encodng tme, wth slght ncrease n. Table V gves the comparsons of the proposed algorthm wth some representatve methods n the lterature. For a clear vew of the comparson n graph, we use to reveal the complexty reducton on encodng tme. As shown n Fgure 6, the smaller value n X axs, whch means the lower, the more tme s saved to encode. And the smaller value n Y axs, whch reveals the lower, the less addtonal bts are used to encode. Therefore, the nearer to the left-down corner n the graph, the better s the performance of the algorthm. Our proposed algorthm has a good performance and s comparable to [7] whch s the state-of-the-art result. Furthermore, our proposed algorthm outperforms [8] n both and tme rato, where [8] also has a good result. In addton, [9] s one of very few algorthms that uses machne learnng technque. Although ts s a lttle bt lower than our result, our complexty gans a 20% reducton. Compared wth [25] and [26], our result also has advantages n both and tme savng APSIPA 1088 APSIPA ASC 2015

5 TABLE IV OVERALL RESULTS OF THE PROPOSED ALGORITHM Classfcaton Sequence Num. of frames All Intra Man ATS Traffc Class A (2560x1600) PeopleOnStreet Nebuta SteamLocomotve Kmono Class B (1920x1080) ParkScene Cactus BasketballDrve BQTerrace BasketballDrll Class C (832x480) BQMal PartyScene RaceHorses BasketballPass Class D (416x240) BQSquare BlowngBubbles RaceHorses Class E (1280x720) FourPeople Johnny KrstenAndSara TABLE V PERFORMANCE COMPARISON WITH EXISTING METHOD Class Average Result [7] [8] [9] [25] [26] Proposed all the pxels n the mage block n our random forests scheme and output the decson on whether the process stays n the current depth for predcton or drectly skps the current CU sze and starts the predcton for the hgher depth. Ths s the core strategy of our algorthm whch greatly reduces the computatonal complexty. Expermental result shows that we can acheve the state-of-the-art result, wth 0.80% ncrease and 48.31% tme savng. Fg. 6 Performance comparson wth exstng methods V. CONCLUSION In ths paper, a fast CU depth decson of ntra-frame codng based on a machne learnng technque s proposed. To begn wth, we have used a weghtng sum approach for partton decson. Ths s acheved by usng neghborng CUs as a reference to set the CU sze range for the current CU. Then, we ntroduce random forests to make a predcton on selectng CU sze 64x64 or 32x32. Before encodng, we nput ACKNOWLEDGMENT Ths work s supported by the Center for Sgnal Processng, the Hong Kong Polytechnc Unversty (G-YBAS) and the Research Grant Councl of the Hong Kong SAR Government: PolyU5243/13E(B-Q38S). REFERENCES [1] B. Bross, W.-J. Han, G. J. Sullvan, J.-R. Ohm, and T. Wegand, Hgh effcency vdeo codng (HEVC) text specfcaton draft 6, Jont Collaboratve Team Vdeo Codng (JCT-VC) ITU-T APSIPA 1089 APSIPA ASC 2015

6 VCEG and ISO/IEC MPEG, San Jose, CA, USA, Aprl. 2012, Doc. JCTVC-H1003. [2] T. Wegand, G. J. Sullvan, G. Bjontegaard, and A. Luthra, Overvew of the H.264/AVC vdeo codng standard, IEEE Trans. Crcuts Syst. Vdeo Technol., vol. 13, no. 7, pp , Jul [3] J.-R. Ohm, G. J. Sullvan, H. Schwarz, T. K. Tan, T. Wegand. "Comparson of the codng effcency of vdeo codng standards ncludng hgh effcency vdeo codng (HEVC)." IEEE Trans. Crcuts Syst. Vdeo Technol., vol. 13, no. 12, pp , Jan [4] Mahsa T., Pourazad, C. Doutre, M. Azm, and P. Nasopoulos. "HEVC: the new gold standard for vdeo compresson: how does HEVC compare wth H. 264/AVC?" Consumer Electroncs Magazne, vol. 1, ssue 3, pp , July, [5] D. Marpe, H. Schwarz, and S. Bosse, Vdeo compresson usng nested quadtree structures, leaf mergng, and mproved technques for moton representaton and entropy codng, IEEE Trans. Crcuts Syst. Vdeo Technol., vol. 20, no. 12, pp , Dec [6] J. Lanema, F. Bossen, W. J. Han, J. Mn, and K. Ugur, Intra Codng of the HEVC standard, IEEE Trans. Crcuts Syst. Vdeo Technol., vol. 22, no. 12, pp , Dec [7] S. Cho and M. Km, Fast CU Splttng and Prunng for Suboptmal CU Parttonng n HEVC ntra codng. IEEE Trans. Crcuts Syst. Vdeo Technol., vol. 23, no. 9, pp , Feb [8] L. Shen, Z. Zhang, and Z. Lu. "Effectve CU Sze Decson for HEVC Intracodng." IEEE Trans. on Image Process., vol.23, no.10, pp , July [9] D. Ruz-Coll, V. Adzc, G. Fernandez-Escrbano, H. Kalva, J. L. Martnez, P. Cuenca. Fast Parttonng Algorthm for HEVC Intra Frame Codng Usng Machne Learnng. pp , Proceedngs, IEEE Intern. Confer. on Image Process. (ICIP2014), Pars, France, Oct [10] B. Mn and R. Cheung, A fast CU Sze Decson Algorthm for the HEVC Intra Encoder. IEEE Trans. Crcuts Syst. Vdeo Technol., vol. 25, no. 5, pp , May [11] L.-L. Wang, and W.-C. Su. Novel adaptve algorthm for ntra predcton wth compromsed modes skppng and sgnalng processes n HEVC. IEEE Trans. Crcuts Syst. Vdeo Technol., vol.23, no.13, pp , Sept [12] L.-L. Wang, and W.-C. Su. "H. 264 fast ntra mode selecton algorthm based on drecton dfference measure n the pxel doman." pp , Proceedngs, IEEE Intern. Confer. on Acoustcs, Speech and Sgnal Process. (ICASSP), Tape, Tawan, Aprl [13] Lang Zhao, L Zhang, Swe Ma and Debn Zhao, Fast Mode Decson Algorthm for Intra Predcton n HEVC, pp.1-4, Proceedngs, Vsual Commun. and Image Process. Confer., Tanan, Tawan, Nov [14] J.-H. Mn, S. Lee, I.-K. Km, W.-J. Han, J. Lanema, and K. Ugur, Unfcaton of the Drectonal Intra Predcton Methods n TMuC, Jont Collaboratve Team Vdeo Codng (JCT-VC) ITU-T VCEG and ISO/IEC MPEG, Geneva, Swtzerland, Jul Doc. JCTVC-B100 [15] L. Zhao, L. Zhang, X. Zhao, S. Ma, D. Zhao, W. Gao. "Further Encoder Improvement of ntra mode decson, Jont Collaboratve Team Vdeo Codng (JCT-VC) ITU-T VCEG and ISO/IEC MPEG, Daegu, Korea, Jan. 2011, Doc. JCTVC-D283. [16] Y. Wang, X. Fan, L. Zhao, S. Ma, D. Zhao, W. Gao. A Fast Intra Codng Algorthm for HEVC, pp , Proceedngs, IEEE Intern. Confer. on Image Process. (ICIP2014), Pars, France, Oct [17] L.Breman, Random Forests, Mach. Learn., vol. 45, no.1, pp.5-32, [18] P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomzed trees, Mach. Learn., vol. 63, no.1, pp.3-42, [19] L. Breman, J. Fredman, C. J. Stone, and R.A. Olshen, Classfcaton and Regresson Trees. Boca Raton, FL, USA: CRC Press, [20] A.Crmns, J.Shotton, and E. Konukoglu, Decson Forests: A unfed framework for classfcaton, regresson, densty estmaton, manfold learnng and sem-supervsed learnng, Found. Trends Compt. Graph. Vs., vol. 7, no. 2-3, pp , [21] F. Bossen, Common Test Condtons and Software Reference Confguraton, document JCTVC-L1100, ITU-T/ISO/IEC Jont Collaboratve Team on Vdeo Codng (JCT-VC), 12 th Meetng, Geneve, CH 14-23, Jan [22] Jont Collaboratve Team on Vdeo Codng Reference Software, ver. HM 10.0 [Onlne]. Avalable: svn/svnhevcsoftware/ [23] G. Bjøntegaard, Calculaton of average PSNR dfferences between RD Curves, n Proc. ITU-T SG16/Q.6 VCEG 13th Meet., Austn, TX, USA, Apr. 2001, Doc. VCEG-M33. [24] G. Bjøntegaard, Improvements of the BD-PSNR model, ITU- T SG16/Q.6 VCEG 35th Meet., Berln, Germany, Doc. VCEG-AI11. [25] M.U.K Khan, M. Shafque, J. Henkel, An Adaptve Complexty Reducton Scheme wth Fast Predcton Unt Decson for HEVC Intra Encodng, pp , Proceedngs, IEEE Intern. Confer. on Image Process. (ICIP2013), Melbourne, Australa, Sept [26] J. Km, Y. Choe, and Y.-G. Km, Fast Codng Unt Sze Decson Algorthm for Intra Codng n HEVC, pp , Proceedngs, IEEE Intern. Confer. on Consumer Electroncs (2013ICCE), Berln, Germany, Sept, APSIPA 1090 APSIPA ASC 2015