Fast Coding Strategy for HEVC by Motion Features and Saliency Applied on Difference Between Successive Image Blocks

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1 Fast Codig Strategy for HEVC by Motio Features ad Saliecy Applied o Differece Betwee Successive Image Blocks Pallab Kati Podder, Maoraja Paul, ad Mazur Murshed Abstract. Itroducig a umber of iovative ad powerful codig tools, the High Efficiecy Video Codig (HEVC) stadard promises double compressio efficiecy, compared to its predecessor H.264, with similar perceptual quality. The icreased computatioal time complexity is a importat issue for the video codig research commuity as well. A attempt to reduce this complexity of HEVC is adopted i this paper, by efficiet selectio of appropriate blockpartitioig modes based o motio features ad the saliecy applied to the differece betwee successive image blocks. As this differece gives us the explicit visible motio ad saliet iformatio, we develop a cost fuctio by combiig the motio features ad image differece saliet feature. The combied features are the coverted ito area of iterest (AOI) based biary patter for the curret block. This patter is the compared with a previously defied codebook of biary patter templates for a subset of mode selectio. Motio estimatio (ME) ad motio compesatio (MC) are performed oly o the selected subset of modes, without exhaustive exploratio of all modes available i HEVC. The experimetal results reveal a reductio of 42% ecodig time complexity of HEVC ecoder with similar subjective ad objective image quality. Keywords: HEVC; Motio Features; Saliecy Feature; Area of Iterest (AOI); Itermode Selectio. 1 Itroductio The prime goal of the latest HEVC [1] stadard is to provide the same perceptual quality compared to its atecedet H.264 [2], at approximately 50% bit-rate reductio for efficiet trasmissio ad storage of high volume video data [3]. HEVC itroduces ivetive approaches, icludig the codig uit (CU) size extesio from up to pixels, variable size predictio uit (PU) ad trasform uit (TU), ad symmetric/asymmetric block partitioig pheomeo have achieved o improved performace gai, but at a cost of more tha 4 times algorithmic complexity for a particular implemetatio [4][5]. For this reaso, ay electroic devices with limited processig capacity could ot fully exploit HEVC ecodig ad decodig features. This motivated us to reduce the computatioal time by appropriate selectio of iterpredictio modes. For this to happe, oly the AOI i a video is take ito accout that comprise with phase correlatio based motio features ad the saliecy feature after applyig it o the differece betwee successive image blocks. Ulike HEVC test

2 model (HM) [6], the selected CUs with, ad without, AOI are motio estimated ad motio compesated with modes i the higher ad lower depth levels, respectively. Thus, i the proposed method, exhaustive exploratio of all modes i each codig depth level ca be avoided (level 64 64, 32 32, ad 8 8 are deoted as depth level 0, 1, 2, 3 respectively). This results i computatioal time reductio. To select a particular motio predictio mode, HM exhaustively checks the Lagragia cost fuctio (LCF) [7] usig all modes i each codig depth level. The LCF, Θ(k ) for mode selectio (k is the th mode) is defied by: ( k ) = D( k ) + R( k ) (1) where λ is the Lagragia multiplier, D(k ) is the distortio, ad R(k ) is the resultat bit, which are determied by modes for each CU. I order to select the best partitioig mode i a codig depth level, HM checks at least 8, ad at most 24, iter-predictio modes with lowest LCF. This process is time cosumig due to the exploratio of all modes i oe or more codig depth levels. Moreover, oly the LCF-based mode decisio could ot always provide the best rate distortio (RD) performace i differet operatioal codig poits because of complex CU partitioig patters, blockpartitioig ad trasformatio headers, codig legth of motio vectors, diversified video cotets, ad other advaced parameter settigs. Therefore, istead of merely depedig o LCF, some cosecutive pre-processig stages are executed by the proposed techique that makes mode decisio process more appropriate ad less time cosumig (illustrated i Fig. 1). I literature, some researchers have cotributed to reduce time complexity [8]-[11]. I order to termiate the explorig modes i lower level, Hou et al. [12] recommed a RD cost based threshold to explore modes oly i the higher level that results i 30% time savigs with 0.5% quality loss. Vae et al. [13] propose a efficiet iter-mode decisio scheme by fidig the cadidate PU modes of symmetric ad asymmetric motio partitio. The tested results reveal the reductio of HEVC ecoder complexity by 31%-51% at the cost of 0.2%-1.3% bit-rate icremet. Pa et al. [14] itroduce a early MERGE mode decisio algorithm to reduce computatioal complexity of HEVC ecoder. Based o all zero block ad motio iformatio, they first apply MERGE mode for the root CUs the for the childre CUs by mode selectio correlatio. They achieve 35% time savigs with the bit-rate icremet of 0.32%, ad quality loss of 0.11 db peak sigal to oise ratio (PSNR). Cassa et al. [15] propose a fast RD optimizatio by itroducig top skip ad early termiatio strategies where time savigs may go up to 45% with a quality loss of 0.10dB. The Eergy cocetratio ratio (ECR) from phase correlatio was extracted ad employed for mode selectio i HEVC by Podder et al. [16] (used i [17] for H.264 stadard). They save computatioal time by sacrificig 0.24 db PSNR o average compared to the full search approach of HM. As ECR stipulates oly the residual error betwee curret block ad motio-compesated referece block, it would ot provide expected compressio results. For more accurate decisio o ME ad MC modes, i this paper, we extract three motio features from phase correlatio. Other tha motio, visual attetive areas are also extracted by exploitig the saliecy feature. Compared to the curret image, as the differece betwee two successive images provides the actual displacemet ad saliet iformatio, we apply the saliecy feature o image differece

3 to capture motio ad saliecy domiated precise areas perceived by huma visual system. Moreover, as there is o differece of color or cotrast i static areas, the image differece produced motio ad saliet iformatio would be the premier optio to determie visual observat areas. The features are icorporated by developig a weighted cost fuctio to actuate AOI-based biary patter for the curret block. The resultat biary patter is the compared agaist a codebook of predefied biary patter templates to estimate a subset of iter-modes. From the selected subset, the fial mode is determied based o their lowest Lagragia cost fuctio. The proposed method ot oly reduces the computatioal time by appropriate selectio of AOI based ME ad MC modes but also demostrates similar subjective ad objective image quality. The remaider of this paper is structured as follows: sectio 2 describes all the key steps of the proposed method; experimetal results ad discussios are detailed i sectio 3, while sectio 4 is the coclusio of the paper. 2 Proposed Mode Selectio Techique The phase-correlatio reders relative displacemet iformatio betwee curret block ad the referece block by Fast Fourier Trasformatio (FFT). We first extract phase-correlatio related three motio features icludig (i) ECR (α), (ii) predicted motio vector (dx,dy) ad (iii) phase correlatio peak (β) ad combie them with saliecy feature (γ) that is applied o the differece betwee successive image blocks. We evolve a uified AOI based cost fuctio usig the ormalized motio features ad weighted average of the saliecy values to determie a uified AOI feature for the curret block. The biary AOI patter from the uified AOI features is the cofigured by applyig threshold. The best-matched patter template is selected usig a similarity metric where the templates correspods to a sub-set of iter-predictio modes. The fial mode from the selected subset is determied by their lowest LCF. The whole process is show as a block diagram i Fig. 1. Fig. 1. Block diagram of the proposed mode selectio process. 2.1 Motio Features Extractio

4 The phase correlatio is calculated by applyig the FFT ad the iverse FFT (IFFT) of the curret ad referece blocks ad fially applyig the FFTSHIFT fuctio as follows: j( F r Fc ) fftshiftifft e (2) where F c ad F r are the Fast Fourier trasformed blocks of the curret C ad referece R blocks respectively ad is the phase of the correspodig trasformed block. Note that Ω is a two dimesioal matrix. We evaluate the phase correlatio peak (β) from the positio of (dx + blocksize/2 + 1, dy + blocksize/2 + 1) as follows: dx blocksize/ 2 1, dy blocksize/ 2 1 (3) where the blocksize is 8 if 8 8-pixel block is used for phase correlatio. The we compute the predicted motio vector (dx, dy) by subtractig blocksize-1 from the (x, y) positio of Ω where we fid the maximum value of Ω. I the matched block geeratio process, we use the phase of the curret block ad magitude of the motiocompesated block i the referece frame ad fially calculate the matched referece block (Ψ) for the curret block by: ifft F e j( F c ) r (4) Ψ Now the displacemet error ( ) is eumerated by: = C - Ψ. (5) We the apply the discrete cosie trasform (DCT) to error ad calculate the ECR (i.e., α) as the ratio of low frequecy compoet ad the total eergy of the error block by: D error / D ) (6) ( _ low error _ total where D error_low ad D error_total represet the top-left triagle eergy ad the whole area eergy of a particular block. Note that the two sides of the top-left triagle are threefourth of the blocksize i.e., 6 i the proposed implemetatio. 2.2 Saliecy Feature Extractio The leadig models of visual saliecy may cosist of (i) extractig feature vectors (ii) formig a activatio map usig the feature vectors ad (iii) ormalizig the activatio map [18]. The saliecy map is operated as a tool (based o [18][19]) ad icorporated ito our codig architecture. The exploited graph-based visual saliecy (GBVS) techique gives us the variace map of AOI based huma visual features for a 8 8-pixel block cosistig of the values ragig from 0 to 1. We extract the saliecy feature, γ, by averagig all values of a give 8 8 block. Our focus is to ecode the AOI based saliet portios with more bits to achieve better compressio ad improve codig performace. 2.3 Cost Fuctio Based AOI Categorizatio After evaluatig the phase correlatio extracted motio features (i.e., α, β ad (dx, dy) ad saliecy extracted variace map (i.e., γ), we fially determie a cost fuctio (i,j) from these four features for (i,j)th block by- (i, j) = ω 1 α(i, j) + ω 2 (1-β) + ω 3 ( dx δ + dy δ ) + ω 4(γ) (7)

5 4 where δ is the maximum block size, ad ω 1 to ω 4 are the weights with i=1 ω i = 1. We iovatively derive weights for each feature adaptively ad do ot cosider all possible weight-combiatio i this experimet. We oly cosider 0.50, 0.25, 0.125, ad weights based o the relative texture deviatio of the curret block agaist that of the whole frame. To calculate the deviatio we apply Stadard Deviatio (STD) both o the curret block ad the curret frame ad use those weights for four attributes. First we sort four features based o their values ad if the value of the STD of the block is smaller tha the value of the curret frame the the highest weight (i.e., 0.50) is applied to the feature 1 (i.e., sorted) ad the lowest weight (i.e., 0.125) is applied to the feature 4 (accordig to sorted list); otherwise, iverse weighted order is applied. If the resultat value of the cost fuctio (i.e., ) is greater tha a predefied threshold the block is tagged by 1 otherwise tagged by 0 where biary 1 ad 0 correspods to AOI ad o-aoi respectively. The ratioality of the proposed weight selectio strategy is that if the curret block has higher texture variatio compared to the curret frame, the curret block should be ecoded with more bits compared to the rest of the blocks to achieve similar/improved RD performace. To esure spedig more bits we eed to categorize the block as AOI block which is doe by our threshold selectio strategy. Other weight selectio approach might work better, however, the experimetal results of the proposed techique reveals similar RD performace (Fig. 7 ad Table 4). (a) Differece betwee 11 th ad 12 th frame o Teis video (b) No Motio (c) Simple motio (d) Complex motio (e) ECR geerated values (f) Saliecy iduced values Fig. 2. Illustratio of motio features ad saliecy feature geerated at differet CUs of 12 th frame o Teis video; (b-d) are the phase shifted plots for o motio (0.4), simple motio (0.7) ad complex motio (0.8); (e-f) correspods to the respective values geerated by ECR ad saliecy map for CUs at positios (3, 1), (3, 10) ad (5, 7) respectively. For clear visualizatio we use block size. The relatioship of the quatitative motio ad saliece features with the huma visual features are depicted i Fig. 2. Fig. 2 (b-d) shows the categories of motio-peak (β) ad their correspodig values provided by ECR (Fig. 2 (e)) ad saliecy feature

6 (Fig. 2 (f)) for Teis video. The GBVS techique applied o the differece betwee successive image blocks produces the resultat cost fuctio based actual saliece maps. These maps are geerated betwee 11 th ad 12 th frame o Teis video for CU at positios (3, 1), (3, 10) ad (5, 7) respectively with its texture deviatio as illustrated i Fig. 3 (a)-(c) ad their respective saliet values are show i Fig. 3 (d)-(f). We level the complex texture ad smooth texture areas by reddish ad bluish colour respectively while ay other colour correspods to simple texture areas. Fig. 4 illustrates the impact of saliecy map ad from Fig. 4 (a), we clearly depict the ellipse like red marked area as the table teis court edge (obviously a visually attetive area) that is precisely idetified by the saliecy feature (red marked ellipse like area i Fig. 4 (c)) although three motio features of phase correlatio could ot grasp the court edge as it does ot have ay motio (white marked ellipse like area i Fig. 4 (b)). Thus, the proposed combied strategy improves the RD performace by recogizig ot oly the AOIbased motio features of phase correlatio but also the AOI-based visually attetive areas iside the videos. (a) Smooth Texture (b) Simple Texture (c) Complex Texture (d) Value of (a) (e) Value of (b) (f) Value of (c) Fig. 3. Saliece maps after applyig GBVS o three differet image blocks with differet motios ad texture of a frame idetified i Fig. 2. (a) Origial image take from 12 th frame of Teis video (b) Motio map idetificatio by applyig three motio features (c) Motio map idetificatio by both motio ad saliecy features Fig. 4. Idetificatio of motio ad saliet areas with ad without saliecy feature based cost fuctio. 2.4 Itermode Selectio For the geeratio of biary matrix, we exploit each of the 8 8 pixel blocks from the pixel blocks (i.e., CU) ad produce a matrix of 4 4 biary values for each CU (applyig threshold). The cost fuctio geerated 4 4 biary matrix is the compared with a codebook of predefied biary patter templates (BPT) to select a subset of modes. Each of the templates is costructed with a patter of AOI ad o-aoi block focusig o the rectagular ad regular object shapes at block level as show i Fig. 5. Both i Fig. 5 (for level) ad Table 1(b) (for level), the cells with

7 black squares preset the AOI (i.e., biary 1) ad the rest are o-aoi (i.e., biary 0). We use a simple similarity metric usig the Hammig Distace (DH) betwee the biary matrix of a CU geerated by phase correlatio ad the BPTs i Fig. 5. We select the best-matched BPT that provides miimum sum of the absolute values of their differeces for a CU. Fig. 5. Codebook of the proposed biary patter templates for subset of iter-mode selectio where template cells with black squares preset AOI (i.e., biary 1) ad the rest are o-aoi (i.e., biary 0). The DH, D is determied as follows where M is the biary motio predictio matrix of a CU comprisig or 0 combiatio ad P is the -th BPT: 4 4 D ( x, y) M ( x, y) P ( x, y). (8) x 0y 0 The best-matched j-th BPT is selected from all BPTs as follows: P j arg mi ( D ). (9) P BPT Table 1. Selectio techique of iter-modes at ad codig depth levels (a) Mode selectio at block level based o predefied biary patter templates (b) Subset of mode selectio at block level based o AOI patter The mode selectio process from the BPTs at ad codig depth levels is illustrated i Table 1 (a) ad (b) respectively. Oce a particular template selects a subset of cadidate modes at level, the fial mode is decided by their lowest Lagragia cost fuctio. Agai, at level, if ay of the codig depth level modes is selected, we further explore smaller modes at level usig the AOI patter (i.e., presece or absece of biary 1 ad 0 as show i Table 1 (b)).

8 The the equatio for the fial mode (ξ) selectio is give by: = arg mi( ( k )) (10) k where Θ(k ) is the Lagragia cost fuctio for mode selectio. 2.5 Threshold Determiatio I lieu of selectig thresholds dyamically, we apply the static threshold value ad fix it by 0.15 i order to elevate the threshold selectio complexity although we cosider both the homogeeous ad heterogeeous motio regios i the CUs of all sequeces. Due to the imbalaced distributio of ECR values Podder et al. [16] (also metioed differet thresholds i [20]-[21]) use a rage of thresholds from 0.37 to 0.52 for differet bit-rates. Those thresholds could ot perform well i the proposed method as the distributio of cost fuctio values is more compact ad almost i all cases it exceeds the value of 0.15 i all types of sequeces for the blocks with motio ad domiat saliece. Therefore, i the proposed techique, we use the fixed value of threshold (i.e., 0.15) for a wide rage of bit-rates. 3 Experimetal Results ad Aalysis To verify the performace of the proposed algorithm, experimetal results are preseted with six stadard defiitio (SD) videos- Teis, Tempete, Waterfall, Silet, Paris, Bridgeclose, four high defiitio (HD) videos- Pedestria, Bluesky, Rushhour, Parkru ad two multiview (MV) videos- Exit ad Ballroom. Each of the video sequeces are ecoded with 25 frame rate ad search legth ±64. We compare the proposed method results with HM of HEVC stadard as HM outperforms the existig mode selectio techiques i the literature. We use IPPP format with Group of picture (GOP) 32 for both techiques ad two referece frames. Table 2. Average time savigs (%) by the proposed method (agaist HM) i terms of mode selectio for each type of sequeces- a theoretical aalysis Sequece types Average o. of itermodes selected per CU by HM Average o. of iter-modes selected per CU by proposed method Average time savigs (%) i terms of mode selectio SD HD MV Average percetage (%) of time savigs Experimetal Setup I this paper, the experimets are coducted by a dedicated desktop machie (with Itel core i GHz, 16 GB RAM ad 1TB HDD) ruig 64 bit Widows operatig system. The proposed scheme ad HEVC with exhaustive mode selectio scheme are developed based o the referece software HM (versio 12.1) [6]. RD performace of both schemes are compared cosiderig the maximum CU size of by eablig both symmetric ad asymmetric partitioig block size of to

9 8 8 depth levels for a wide rage of bit-rates (usig QP=20, 24, 28, 32 ad 36). I the proposed approach, each CU is set as a pixel block ad we use 8 8 pixel block for biary matrix geeratio. The calculatios of BD-PSNR ad BD-Bit Rate were performed accordig to the procedures described i [22]. 3.2 Results ad Discussios For the theoretical justificatio of computatioal time of all type of sequeces, we first compare the average umber of modes selected i each CU by HM ad the proposed method. The results i Table 2 shows that HM checks more optios i all cases ad ormally requires more computatioal time. From Table 2, the overall average percetage of ecodig time savigs by the proposed method is ad the reaso of this acquisitio is the efficiet subset of itermode selectio with simple criteria. However, we caot igore the pre-processig stages of the proposed method, ad by calculatio we otice that over twelve sequeces o average 6.71% extra ecodig time is required to execute phase correlatio ad saliecy related preprocessig overheads (see Fig. 1). Thus, theoretically we aticipate to acquire 43.04% computatioal time savigs o average. (a) Time savigs based o differet Qp (b) Time savigs based o sequece types Fig. 6. Illustratio of time savigs by the proposed method agaist HM. The experimetal evaluatio reveals that over twelve differet sequeces, ad for a wide rage of bit-rates, the proposed method reduces o average 42% (rage: 37%- 45%) computatioal time as show i Fig. 6 (a). The equatio for the time savigs (TS) is defied as: TS = (T 0 T p ) T 0 100% (11) where T 0 ad T p deote the total ecodig time cosumed by HM ad the proposed method respectively. For comprehesive performace test, we execute the computatioal time aalysis of both techiques based o video categories ad fid that the proposed method achieves o average 41% ecodig time savigs compared to HM as show i Fig. 6 (b). The figure also reveals that the proposed techique saves more time for SD video type (48%) compared to HD (40%) or MV videos (36%). Table 3. Modes selectio compariso by HM ad proposed method at three codig depth levels

10 Fig. 7. Comparative study o RD performace by HM ad the proposed method for a wide rage of bit-rates. Table 3 shows the average percetage of three differet depth level modes for twelve sequeces at QP=20 to 36. For the proposed method, the higher depth level modes (16 16 ad 8 8) are exploited for the CUs with motio ad domiat saliece due to acquire similar/improved image quality. To test the performace of the proposed method objectively, we first compare the RD performace agaist HM usig three differet sequece types (oe SD, HD ad MV) for a wide rage of bit-rates as demostrated i Fig. 7. The figure shows that the proposed method achieves similar RD performace with HM especially carig about the AOI based CUs ad partitioig them by efficiet selectio of appropriate block partitioig modes. Table 4 represets the performace compariso of the proposed method for twelve diverget video sequeces. The results reveal that compared to the mode selectio approach i HM, the proposed techique achieves a almost similar RD performace (small average reductio of 0.021dB PSNR) with a egligible bit-rate icremet of 0.14%. Table 4. Performace compariso of proposed techique compared to HM usig BD-Bit Rate ad BD-PSNR BD- BD- BD- BD- Video Video PSNR Bit Rate PSNR Bit Rate Sequeces Sequeces (db) (%) (db) (%) Teis Pedestria Tempete Bluesky Waterfall Rushhour Silet Parkru Paris Ballroom

11 Bridgeclose Exit Average BD-PSNR ad BD-Bit Rate over twelve sequeces Fig. 8(a) shows the origial image of Teis video take for subjective quality test ad Fig. 8(b) ad Fig. 8(c) illustrate the reproduced images by HM ad the proposed method respectively. To preset the compariso i image quality let us cocetrate o the cuff ad sleeve sectios of the shirt i the three images which are marked by the red, yellow, ad white ellipses respectively. It ca be perceived that the three ellipse marked sectios have almost similar image quality. It was preseted earlier i this mauscript that the proposed method requires less ecodig time. Hece it ca be cocluded that the proposed techique shows sigificat computatioal time savigs compared to HM12.1 with similar image quality for a wide rage of bitrates. Due to this pheomeo, the proposed implemetatio is expected to become more suitable for all real time video codig applicatios especially for a umber of electroic devices with limited processig power ad battery capacity. (a) Origial image of Teis (b) HM reproduced image (c) Proposed method reproduced sequece image Fig. 8. Subjective quality assessmet for HM ad the proposed method for Teis video sequece. The figures are achieved from the 20 th frame of the Teis video at the same bit-rate.

12 4 Coclusio I this work a ovel codig framework for HM performace improvemet is preseted. This is implemeted by exploitig the AOI based mode selectio techique comprisig three differet motio features of phase correlatio ad saliecy features of huma visual attetio. The motio features focus o three differet aspects of motios i each CU ad the saliecy feature captures the visual attetive areas. A adaptive cost fuctio is formulated to determie a subset of iter-modes usig predefied AOI based biary patter templates. The Lagragia optimizatio criterio is employed i the selected subset of modes to fix the fial mode. Compared to HM with exhaustive mode selectio strategy, the proposed scheme reduces o average 42% computatioal time (rage: 37%-45%) while providig similar image quality for a wide rage of bit-rates. Refereces 1. High Efficiecy Video Codig, documet ITU-T Rec. H.265 ad ISO/IEC (HEVC), ITU-T ad ISO/IEC, April T. Wiegad, G. J. Sulliva, G. Bjotegaard ad A. Luthra, Overview of the H.264/AVC Video Codig Stadard, IEEE Trasactios o Circuits ad Systems for Video Techology, vol. 13, o. 7, pp , July Bross, Ha, W. J. Ohm, J.R. Sulliva, ad G. J. Wiegad, High Efficiecy Video Codig Text Specificatio Draft 8, JTCVC- J1003, Swede F. Bosse, B. Bross, K. Suhrig, ad D. Fly, HEVC Complexity ad Implemetatio Aalysis, IEEE Trasactios o Circuits ad Systems for Video Techology, vol. 22, o. 12, pp , December Y. Lu Real-Time CPU based H.265/HEVC Ecodig Solutio with Itel Platform Techology, Itel Corporatio, Shaghai, PRC, December Joit Collaborative Team o Video Codig (JCT-VC), HM Software Maual, CVS server at: ( M. Paul ad M. Murshed, Video Codig Focusig o Block Partitioig ad Occlusios, IEEE Trasactios o Image Processig, vol. 19, o. 3, pp , March A. Lee, D. S. Ju, J. Kim, ad J. Seok, A Efficiet Iterpredictio Mode Decisio Method for Fast Motio Estimatio i HEVC, Iteratioal Coferece o ICT Covergece (ICTC), pp , October H. L. Ta, F. Liu, Y.H. Ta, ad C. Yeo, O Fast Codig Tree Block ad Mode Decisio for High Efficiecy Video Codig, Iteratioal Coferece o Acoustic ad Speech Sigal Processig (ICASSP), pp , March J. Leg, L. Su, T. Ikeaga ad S. Sakaida, Cotet Based Fast Codig Uit Decisio Algorithm for HEVC, i proc. IEEE Iteratioal Coferece o Multimedia ad Sigal processig, pp , May G. Correa, P. Assucao, L. Agostii, ad L. D. S. Cruz, Complexity Cotrol of High Efficiecy Video Ecoders for Power-Costraied Devices, IEEE Trasactios o Cosumer Electroics, vol. 57, o. 4, pp , November X. Hou, ad Y. Xue, Fast Codig Uit Partitioig Algorithm for HEVC, IEEE Iteratioal Coferece o Cosumer Electroics (ICCE), pp. 7-10, 2014.

13 13. J. Vae, M. Viitae, ad T. Hamalaie, "Efficiet Mode Decisio Schemes for HEVC Iter Predictio," IEEE Trasactios o Circuits ad Systems for Video Techology, vol. 24, o. 9, pp , Z. Pa, S. Kwog, M. T. Su ad J. Lei, Early MERGE Mode Decisio Based o Motio Estimatio ad Hierarchical Depth Correlatio for HEVC IEEE Trasactios o Broadcastig, vol. 60, o. 2, pp , Jue M. B. Cassa, M. Naccari, ad F. Pereira, Fast Rate Distortio Optimizatio for the Emergig HEVC Stadard, Picture Codig Symposium, pp , May P. Podder, M. Paul, M. Murshed, ad S. Chakrabarty, Fast Itermode Selectio for HEVC Video Codig Usig Phase Correlatio, IEEE Iteratioal Coferece o Digital Image Computig: Techiques ad Applicatios (DICTA), pp. 1-8, November M. Paul, W. Li, C.T. Lau, ad B.S. Lee, Direct Iter-Mode Selectio for H.264 Video Codig Usig Phase Correlatio, IEEE Trasactios o Image Processig, vol. 20, o. 2, pp , J. Harel, C. Koch, ad P. Peroa, Graph-Based Visual Saliecy, Califoria Istitute of Techology, Pasadea, CA , ad explored date: Jauary M. Paul, M. Frater, ad J. Arold, A Efficiet Mode Selectio Prior to the Actual Ecodig for H.264/AVC Ecoder, IEEE Trasactios o Multimedia, vol. 11, o. 4, pp , April L. To, M. Pickerig, M. Frater, ad J. Arold, A Motio Cofidece Measure from Phase Iformatio, IEEE Iteratioal Coferece o Image Processig, pp , Bjotegaard, Calculatio of Average PSNR Differeces Betwee RD curves, ITU-T SC16/Q6, VCEG-M33, Austi, USA, 2001.