Inter Mode Accelerating Decision Algorithm for HEVC

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C K eyLaboratoryofSigna landInformaitonProcessing,ChongqingUniverstiyofPosts a

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*Correspondingauthor

w y e

K ords: High efifciency video coding, Prediciton unti, Moiton feature, Spaito-tempora l

n o it a l e r r o

c .

.t c a r t s b

A Inthispaper,proposes afas tintermodedecisionalgorithmforHEVCencoder .According y

t i r a l i m i s e h t o

t , betweent hecurren tcodinguni t(CU)dept handt heC Uoft hespatio-tempora land h

t p e d s u o i v e r p e h

t C Ucorrespondingt ot hepredictionunit ( UP )mode ,tor educet hecurren tPUmode g

n a r l a s r e v a r

t e .ComparedwithHEVCt es tmodel(HM)16.9 ,theresul tshowst hatt healgorithmcan g

n i d o c e h t e c u d e

r timeby4 98. 7%onaverageandensuret heoutpu tbi trateoft hecodingi sl essand o

i t a r e s i o n o t l a n g i s k a e p e h

t (PSNR)isbasicallyunchanged.

n o it c u d o r t n I

h g i h f o h t w o r g e h t h t i

W -definition(HD)andhigh-framedigita lvideoconsumerdemands ,people s

o p o r

p e higher demands on the compression efficiency of digita lvideo .In order to mee tthe ,l

i r p A n O , a t a d o e d i v D H s s a m r o f s t n e m e r i u q e r e g a r o t s d n a n o i s s i m s n a r

t 2013 ,Join tCollaborative

g n i d o C o e d i V n o m a e

T ( TJC - CV ) officially released a new generation of hig -h efficiency video d n a g n i m r o f s n a r t , g n i t c i d e r p h t i w k r o w e m a r f d i r b y h e h t s t p o d a l l i t s C V E H . d r a d n a t s ) C V E H ( g n i d o c

p o o l e v i t p a d a , n o i s i v i d e c i l s d n a r a b , e r u t c u r t s e e r t d a u q e l b i x e l f g n i s u h g u o r h t , g n i d o c y p o r t n e

m i y l t a e r g , s e i g o l o n h c e t r e h t o d n a g n i r e t l i

f proving the video coding efficiency .Comparing with /

4 6 2 .

H AVC ,HEVCcansavemoret han50%oft heencodingbi tstream ][1 _o_n_t_h_e_s_a_m_e_v_i_d_e_o_q_u_a_l_i_t_y_,

f o t s o c e h t t

a thecodingcomputationi si ncreasedby2t o3t imes .Int heHEVCframework ,statistics e

g g u

s stt ha,tt hecomputationa lcomplexity on intercodingi sapproximately69%oft hewholecoding s

s e c o r

p . Therefore ,i n recen tyears, many scholarshav estudiedtheinterprediction modedecision e

s o p o r p d n a , m h t i r o g l

a d tl s o ofi ntercodingfas talgorithms. Int hel iterature[2], skippe dsomei nter s

s e c o r p g n i d o c n e e h t p u d e e p s o t s e d o

m , based on the characteristicoftextureand motion in the [

e r u t a r e t i l e h t n I . k c o l b t n e r r u

c 3] ,PU modes selection were early terminated by setting the e

t a

r -distortion threshold .In the literature [4] ,the selection of the depth of the curren tCU w as d

e t a n i m r e

t inadvancebyt hemotioncharacteristic.Int hel iterature [5 ,] tookadvantageofCUdepth o

i t a p

s -tempora lcorrelationt opredictt hecurren tCUdepthrange, toreducet het raversalt imes. n

I th si paper ,makesful lu seofthe correlation between the video motion characteristicand PU s

o p o r p , s e p y t n o i t i t r a

p e sani ntermodeacceleratingdecisionalgorithm .Accordingt ot her elationship n

o i t o m e h t d n a e d o m r e t n i e h t n e e w t e

b characteristic, correspondingt ot heprevious depthCUand tis l

a s r e v a r t e h t , U C t n e r r u c e h t f o U C t n e c a j d

a process fo some PU modes would be ahead of

t a n i m r e

t i ngonthemotionhomogeneousarea ,onsomeofthemoderating andfas tmotion regions, U

P e m o

s modesmigh tbeskipped. Thusreducingt henumberofPUmodeselection, todecreasethe .

y t i x e l p m o c g n i d o c

e h

T Feature so fCUandPUi nHEVC

e , C V E H n

I ach coded imageiscomposed ofmanyslices ,each sliceisdivided intoaCodingTree t

i n

U (CTU),witht hedepthi 0s ,whichi sa64x64pixelsCU .ACTUcancontinuet obedividedi nto 3

d n a 2 , 1 g n i e b h t p e d h t i w U C r e l l a m

s ,correspondingCUsizea re32x32pixels ,16x16pixelsand 8

x

(2)

e g r e M p i k

S 2N�2N N�N 2N�N

N

2 �nD 2N�nU nL�2N nR�2N N�2N

N

2 �2N N�N

r e t n i

a r t n i

g i

F u .re1 ThePUdividingt ypesinHEVC.

E ha c CUcancontinuet obedividedi ntot woorfourPUsi nFigure1 .ThePUmodei ncludi nginter h

c i h w , l a c i r t e m m y s s i e d o m n o i t c i d e r p a r t n i e h T . e d o m a r t n i d n a e d o

m including2N×2NandN×N.

e d o m n o i t c i d e r p r e t n i e h

T h assixsymmetricmodesandfourasymmetricmodes ,includingskip , N 2 × L n r e t n i , D n × N 2 r e t n i , U n × N 2 r e t n i , N × N r e t n i , N 2 × N r e t n i , N × N 2 r e t n i , N 2 × N 2 r e t n i , e g r e m

e h T . N 2 × R n r e t n i d n

a fourlattera reasymmetricmode .Theasymmetricmodei sdividedtheCUarea 3

: 1 o t n

i , rf o enhanci ngtheaccuracyoni nterprediction, ht su increaset hecodingefficiency.Merge e

d o m n o i t c i d e r p r e t n i d e c u d o r t n i t s e t a l e h t s i e d o

m i n HEVC .Themotionparametersareprocessed h

g u o r h

t "motioncombining"t echnique ,onlytransmittingtheindex valueoftheselectedPUblock . n

o i t o m e h

T informationi sobtainedbyt hemotionestimationmethodatt hedecoder[6]_.S_k_i_p_m_o_d_e_i_s_a

e d o m e g r e m e h t e r e h w e s a c l a i c e p

s coded block flag is zero. HM adopts theful lsearch traversa l d

o h t e

m .Atf irs,tt raversingallt hedepthi nt heCUl ayer, then ,calculatingt hecorrespondingcandidate e

n i e u l a v n o i t r o t s i d e t a r U

P very recursive process, finally, selecting the minimum rate distortion s

a U P g n i d n o p s e r r o

c thebes tPU .Thismethod cange thighqualityvideo ,bu tgreatly increasesthe l

a c g n i d o c f o t n u o m

a culation. Inview fo therei sacertainexten tdepthcorrelationbetweent hecurren t e

h t n i U C e h t d n a U

C spatio-tempora lfield,t hecorrespondingPUmodes alsohav eagreatsimilarity l

a i t a p s n

i partitioning.

e h

T Correla itonoft heCurren tCUandAdjacen tCU

[ e r u t a r e t i

L 7]definest hepredictingcandidatese tSf ort hecurren tCU( CU_c_u_r_r_e_n_t), asshowni nequation )

1

( , accordingt ot hecorrelationbetweent hecurren tCUdepthvaluesizeandi tsadjacen tCUint he o

i t a p

s -tempora lfield.

{ =

S CU0, UC 1, UC 2 , UC 3, UC 4, UC 5, UC 6, UC 7} )( 1

e r u g i F n

I 2, UC 0 ,CU1 ,CU2andCU3denotesont hel ef,tl eft-upper ,upperandright-uppersidesi n

, U C t n e r r u c e h t f o d l e i f l a i t a p s e h

t respectively .CU4andCU5aret hecorrespondingCUt ot heCU_c_u_r_r_e_n_t_, e

h t n

i _,forwardreferenceframeandt hebackwardreferenceframeandt hebackwardreferenceframe , U

C 6 Xin -1fromt hecurren tCU ,CU7 Xin -2fromt hecurren tCU ,whilet hecurren tCUdepthi sX.

1 1

0 =

∑

−

=

N

i

α

i

(2)

e h t n

I formula(2) , idenotescandidateCUscorrespondsthesubscrip tnumber. N representsthe ,

s U C e t a d i d n a c f o r e b m u

n Ntakes 8 . α_i representing the weight factor value of sequence i from

U C e t a d i d n a

c .Byencodingal argenumberoft es tsequences,t hecorrelationbetweent hecurren tCU e

h t d n

a adjacentCUi nthespatio-tempora lfield ,upperdepthCUiscalculated ,thenobtainsallthe s

t n e i c i f f e o c n o i t a l e r r o c s U

C in Table 1 .In accordance with the data in Table 1 .assignments i

t a p

(3)

g i

F u .re2 Thespatio-tempora ladjacen tCUandupperdepthCUfromt hecurren tCU. b

a

T l 1 .e Thecorrelationcoefficien tbetweent hecurren tCUandt hecandidateCUs.

i 0 1 2 3 4 5 6 7

i

λ 0.78 0.70 0.76 0.68 0.65 0.64 0.75 0.64

b a

T l 2. e ThecandidateCUs andassigningweigh tfactors.

i 0 1 2 3 4 5 6 7

i

α 0.30 0.15 0.25 0.10 0.10 0.10 0.20 0.10

r e t n

I Mo Ade ccelera itngPrediciton

n o i t i t r a p l a i t a p s n

I ing ,the PU modes traversed by the curren tCU and the spatio- et mpora lfield a

s a h U C t n e c a j d

a grea tsimilarcorrelation ,andt hereare11PUmodescorrespondingt oeachCU .If e

h t o t e d o m U P f o r e b m u n e g n a r g n i s r e v a r t e h t e c u d e

r _C_U_c_u_r_r_e_n_t,t heintermodeselectingprocesswil lbe l a i t a p s e h t n I . g n i d o c r e t n i n o y t i x e l p m o c n o i t a l u c l a c t n u o m a e h t s e s a e r c e d t a h t o s , d e t a r e l e c c a

e h t f o e s u a c e b , n o i s i v i

d CU_c_u_r_r_e_n_t hasbeeni ncludedi nt heCU6 ,hast exturesimilarityrelationshipwith U

C 0 ,CU1 ,CU2andCU3 ,andhasamotionconsistencyr elationshipwithCU4orCU5int emporalf ield . U

C n e h w , e r o f e r e h

T 4 hasthesamedepth as theCU_c_u_r_r_e_n_t or CU6issmalleronethan the CU_c_u_r_r_e_n_ta tthe

m U P g n i d n o p s e r r o c e h t , h t p e

d odesofCU4orCU6canbeusedast het raversa lrangeoft hecurren t .

s e d o m U

P Inordert of acilitatet hepresentation ,ninei nterpredictionmodesandt woi ntraprediction e

l b a T n i d e d i v i d e r a s e d o

m 3 ,andt heyarerepresentedbyt hecorrespondingsigns. b

a

T l .e3 ThePUmodespartitioningstructureandcorrespondingsigns. U

P Inter Intra

Pattern n o i s i v i

d Skip 2N×2N 2N×N N×2N 2N×nU 2N×nD nL×2N nR×2N N×N 2N×2N N×N g

n i d n o p s e r r o C

r e b m u

n m0 m1 m2 m3 m4 m5 m6 m7 m8 m9 m10

t n o s c i t s i r e t c a r a h c n o i t o m n o d e s a

B hevideocodingarea,t hePUmodest raversa lprocessofCU_c_u_r_r_e_n_t

s w o l l o f s a e r

a (theflowchar tof PUmodei nFigure3 : ) )

1 Firstly,j udgingwhethert hereexistsmodem0int hecorrespondingPUmodeofCU0 ,CU2 dan U

C 6(asisshowninFigure2);ifi tis ,theCU_c_u_r_r_e_n_t can bedeterminedstationaryregionorinthesame

s e d o m U P e h t , e s a c s i h t t A . e l p m i s y l e m e r t x e s i e r u t x e t o e d i v d n a n o i t o m w o l s y l e m e r t x e n o i t c e r i d

e h t r o f d e s r e v a r t e b o t d e d e e n e r a 1 m d n a 0

m _C_U_c_u_r_r_e_n_t .Otherwise ,followingt hestepsbelow. )

2 Ifthedepth ofCU_c_u_r_r_e_n_t isequa lto the depth ofCU4 ,which indicatescorresponding to theCU s p e t s g n i k a m , y c n e t s i s n o c n o i t o m e m a s e v a h e m a r f t n e r r u c d n a e m a r f e c n e r e f e r d r a w r o f e h t n i k c o l b

: s w o l l o f s a

I.Whent hecorrespondingPUmodeofCU4 mis 1,i ti st heareawheret hecorrespondingCUblock

e h t , e s a c s i h t t A . e l p m i s s i e r u t x e t o e d i v e h t d n a n o i t o m s u o e n e g o m o h a s a

h CU_c_u_r_r_e_n_t needtotraverse

m e r a e d o m U

(4)

e r e h t f

I existsaPU U C n i e d o

m 0 ,CU2

d n

a CU6ism0

g n i t a l u c l a

C ΔD= |CUcurrent- UC 4 |

o

t determinevaluesize g

n i s r e v a r T m0andm1

n i g e B

U P e h t g n i n i a t b O

U C f o e d o

m 4

m g n i s r e v a r

T 1,m2,

m3andm7

T < C M 0

T2<MC

ΔD=1,2

T1<MC<T2

T0<MC<T1

ΔD=0

ΔD=3

g n i s r e v a r t p u d n E

U P modes N

Y

Y Y

Y Y Y

Y Y

N N N

N N

N

N N

N N U

P modei sm1

U

P modei sm2

U

P modei sm3

U

P modei sm4

U

P modei sm5

U

P modei sm6

m g n i s r e v a r

T 0

m g n i s r e v a r

T 1,m2,

m3,m4andm5

m g n i s r e v a r

T 1,m2,

m3,m6andm7

m g n i s r e v a r

T 1,m2,

m3andm4

m g n i s r e v a r

T 1,m2,

m3andm5

m g n i s r e v a r

T 1,m2,

m3andm6

m g n i s r e v a r

T 1,m2,

m3andm8

U

P modei sm7

U

P modei sm8

g n i s r e v a r T m0andm1

m g n i s r e v a r

T 4,m5,

m6,,m7,m8andm10

g n i d n o p s e r r o c e h t g n i s r e v a r T

e p y t e d o m n o i t c i d e r p

C M e h t o t g n i d r o c c

a value

g i

F u 3. re Theflowchar toft hei ntermodeacceleratingalgorithm.

I

I .Whent hecorrespondingPUmodeofCU4 mis 2,i ti st heareawheret hecorrespondingCUblock

e h t , e s a c s i h t t A . t a l f e t i u q s i e r u t x e t o e d i v e h t d n a n o i t o m e t a r e d o m a s a

h CU_c_u_r_r_e_n_t needt ot raversePU

m e r a e d o

m 1 ,m2 ,m3 ,m4andm5.

I I

I .When thecorresponding PU modeofCU4 mis 3 ,i tistheareawherethecorresponding CU

e h t , e s a c s i h t t A . t a l f e t i u q s i e r u t x e t o e d i v e h t d n a n o i t o m e t a r e d o m a s a h k c o l

b CU_c_u_r_r_e_n_t need to

m e r a e d o m U P e s r e v a r

t 1 ,m2 ,m3 ,m6andm7.

V

I .When thecorresponding PU modeofCU4 mis 4 ,i tistheareawherethecorresponding CU

e h t , e s a c s i h t t A . x e l p m o c e r o m s i e r u t x e t o e d i v e h t d n a n o i t o m t s a f a s a h k c o l

b CU_c_u_r_r_e_n_t needt ot raverse

m e r a e d o m U

P 1 ,m2 ,m3andm4.

V .Whent hecorrespondingPUmodeofCU4 mis 5,i ti st heareawheret hecorrespondingCUblock

e h t , e s a c s i h t t A . x e l p m o c e r o m s i e r u t x e t o e d i v e h t d n a n o i t o m t s a f a s a

h _C_U_c_u_r_r_e_n_t needt ot raversePU

m e r a e d o

m 1 ,m2 ,m3andm5.

I

V .When thecorresponding PU modeofCU4 mis 6 ,i tistheareawherethecorresponding CU

m e r a e d o m U

P 1 ,m2 ,m3andm6.

I I

(5)

m e r a e d o m U

P 1 ,m2 ,m3andm7.

I I I

V .WhenthecorrespondingPUmodeofCU4 mis 8 ,i tistheareawherethecorrespondingCU

m e r a e d o m U

P 1 ,m2 ,m3andm8.

f o e u l a v h t p e d e h t f I )

3 CU_c_u_r_r_e_n_t andCU4isdifferen tandt hedifferencevalueoft hemi sequalt o1or ,

2 whichmeanst herei sal ittledifferencei nt hePUmodepartitioningbetweent heCU_c_u_r_r_e_n_t andadjacen t

, l a r e n e g n I . d l e i f l a r o p m e t e h t n i U

C t hefastermotioni s,t hesmallersizei sdividedonPUmodes .In e

h t f o e g n a r e h t t c i d e r p o t r e d r

o _C_U_c_u_r_r_e_n_t correspondingPUmodeaccurately,t hepaperdefinest heMC ,

e d o m U P f o s e p y t x e l p m o c e h t e b i r c s e d o t ) y t i x e l p m o c e d o m

( TheMCvaluesizei scalculatedi nt he ,

) 3 ( a l u m r o f e h t n I . y t i x e l p m o c n o i t o m e h t f o r e h g i h e h t , s i C M r e g g i b e h T . ) 3 ( a l u m r o

f N is the

s U C e t a d i d n a c f o r e b m u

n int hepredictedcandidatese tS ,hereNtakes8. k_i isusedt oj udgewhether

f i , e l b a l i a v a s i U C e t a d i d n a c e h

t _k_i isequa lto1,whichindicatestha tthecandidateCUisavailable ;

i

kis equa lto 0 ,which means candidate CU information is no tavailable .ωi is the weigh tfactor

d n a , s e p y t n o i t c i d e r p t n e r e f f i d e h t o t g n i d n o p s e r r o

c α_i ist heweigh tfactorofcandidateCUfromt he a

T n i i r e b m u

n b 2. le

i N

i i i k C

M =

∑

−ω × ×α

= 1

0

) 3 (

o t g n i d r o c c

A the partitioning structure relationship from the selected PU, the 11 differen t i

t c i d e r

p on modesin Table 4 arecombined into 4 kindsofprediction types. Basing on the motion n

e h t , y t i x e l p m o

c se tthe weigh tfactor values to 0.5 ,1.0 ,1.5 and 3.0 ,respectively. A nd al lthe n

o i t c i d e r

p typesandcorrespondingPUmodes ea r showni n Table4.

b a

T le 4. Thedifferen tmodet ypessett hecorrespondingweigh tfactors.

t e

S Complexityi ntervals Traversalmodes

S MC<T0 m0

T T0<MC<T1 m1 a m9 nd U T1<MC<T2 m2 a m3 nd V T2<MC m4, m5, m6, m7, m8 andm10

Table5 .Fourkindsofmotiont ypesandcomplexityi ntervals. s

e p y t U

P PUmodes Weigh tfactor

1 m0 0 .5

2 m1 andm9 1 .0

3 m2 andm3 1 .5

4 m4 ,m5 ,m6 ,m7 ,m8 andm10 3 .0

Accordingt ot hemotioni ntensitydividecodingareai ntof ourkindsofcomplexityi ntervals ,which ,

V d n a U , T , S t e s e h t y b d e t o n e d e r

a respectively .Thecriticalt hresholdsi nt heeachmotiont ypeare T

y b d e t o n e

d 0 ,T1 ,andT2 ,respectively .Basedon theempirica lvaluesobtained bycoding thetes t r

r o c s l a v r e t n i y t i x e l p m o c e h T . y l e v i t c e p s e r , 0 . 4 d n a 0 . 2 , 8 . 0 o t t e s e b n a c y e h t , s e c n e u q e

s espondingt o

e l b a T n i n w o h s e r a s n r e t t a p l a s r e v a r t e h

t 5.

)

4 If the difference value between the depth of the CU_c_u_r_r_e_n_t and CU4 is 3 ,which indicates the s

e d o m U P e h T . g n i n o i t i t r a p l a i t a p s e h t n i e c n e r e f f i d t a e r g a e v a h s e d o m U P g n i d n o p s e r r o

c of CU_c_u_r_r_e_n_t

U C f o s e d o m U P e h t t p o d a t o n o

d 4,t erminatingt hePUmodespartitioningoft heCU_c_u_r_r_e_n_t ,andt urnst o

(6)

l a t n e m i r e p x

E Resu tl sandAnalyssi

t a u l a v e o t r e d r o n

I et heproposedalgorithm,t hepaperappliestoHM16.9withrandomaccess(RA) .

n o i t a r u g i f n o

c Thebasicparametersoft hecodingendareasf ollows:t hemaximumCUsizei s64, the 3

s i h t p e d n o i s i v i d m u m i x a

m , thequantizationparameter(QP)takes22, ,2 7 32and37, respectively , o

t r e f e r s n o i t i d n o c t s e t d n a s r e t e m a r a p n o i t a r u g i f n o c r e h t

o literature[8]. Fort estingt heaccuracy,t here s

p u o r g 5 1 e r

a tes tsequence weredivided into four classes :A ,B ,C and D. T he testing hardware m

r o f t a l

p is PQA600Apicture quality analyzerTektronix from Tektronix ,which isconfigured for 2

1 -coreCPU ,clockspeedis2.30GHz ,memoryis32GB ,64-bi tWindows7operatingsystem.Inorder t i b a l t e d d r a a g e t n j B , s P Q r u o f r e d n u e c n a m r o f r e p n o i t r o t s i d e t a r g n i d o c f o n o i t a i r a v e h t t s e t o

t rate

R B D B

( )andBjntegaarddetlapeaksignalt onoiseratio(BDPSNR)[9]_w_e_r_e_u_s_e_d_a_s_t_e_s_t_i_n_d_e_x_e_s_. b

a

T l 6. Ce omparisonoft heproposedalgorithmandl iterature[10] withdifferentt ypes.

s e p y

T Tes tsequence

o t e v i t a l e r m h t i r o g l a d e s o p o r p e h T

9 . 6 1 M H

i l e h

T terature[10]algorithmrelative 9

. 6 1 M H o t R

B D

B (%) BDPSNR( Bd ) ∆T

(%) BDBR(%) BDPSNR( Bd )

T

∆ (%) A

s s a l

C Traffic 1.89 -0.04 54.36 1.63 -0.04 49.44 t

e e r t S n O e l p o e

P 3.05 -0.10 51.18 2.25 -0.12 40.14 B

s s a l

C Cacus 2.32 -0.04 52.90 1.38 -0.02 48.80 e

v i r D l l a b t e k s a

B 2.44 -0.05 53.83 1.54 -0.02 46.71 e

c a r r e T Q

B 0 -0.02 53.40 0.20 -0.01 50.76 o

n o m i

K 1.42 -0.03 50.82 1.05 -0.03 44.21 e

n e c S k r a

P 1.87 -0.04 51.24 1.46 -0.03 48.71 C

s s a l

C BasketballDrill 2.19 -0.05 51.28 1.62 -0.08 42.93 l

l a M Q

B 2.31 -0.06 51.27 2.11 -0.10 44.72 e

n e c S y t r a

P 1.65 -0.06 50.32 1.57 -0.06 39.68 C

s e s r u o H e c a

R 3.34 -0.11 40.08 2.69 -0.09 33.75 D

s s a l

C BasketballPass 2.63 -0.08 47.85 3.04 -0.07 38.58 s

e l b b u B g n i w o l

B 2.19 -0.06 45.88 1.95 - 0.10 40.99 e

r a u q S Q

B 1.13 -0.03 40.75 1.20 - 0.03 32.10 s

e s r o H e c a

R 3.92 -0.13 39.40 3.24 - 0.14 30.68 e

g a r e v

A - 2.16 -0.06 48.97 1.80 - 0.06 42.15

e h

T proposed algorithm and et h literature [ 0] 1 algorithm ea r implemented on HM16.9 ,and the e

c n a m r o f r e

p comparison si shown in Table6,T heaveragecoding timea rereduced by 4 98. 7%and 42. 51 % ,respectively .Theaveragecodingoutpu tbitr ateofproposedalgorithmi si ncreasedby2.16% ,

e r u t a r e t i l e h t n a h t r e h g i h y l t h g i l s s i h c i h

w [10].Thevideodistortionhasnodifferencebasicallyand 0

. 0 y b d e s a e r c e d R N S P e g a r e v a e h

t 6d B. For the intense tes tsequences ,such as PeopleOnStreet , ,l

l i r D l l a b t e k s a B , e v i r D l l a b t e k s a

B RaceHoursesC , BasketballPass and RaceHorses ,the average f

o e m i t g n i d o

c proposedalgorithmi sr educedby4 27. 7% ,andt heliterature 1[ 0]i sr educedby3 . 088 % , t

a h t s e t a c i d n i t

I int heintense om tionsequences ,theproposedalgorithmismuchmoreeffective to g

n i d o c n e e c u d e

(7)

n o is u l c n o C

Tor educet hecodingt imeon HEVCeffectively,throughtheanalysisPUmodes selecitn gprocess, ht e i

t a r e l e c c a n a s e s o p o r p r e p a

p n galgorithmon interpredictionmode.Takingadvantagesofsimliartiyof U

P e h

t modesoft heCUint heupperdepthandadjacen tspatio-tempora lfield fo thecurren tCU, to e

c u d e

r thet raversa lprocess onPUmodes.Ther esul tshows thatt heproposedalgorithmsavingl otsof e

m i t g n i d o

c , att hecaseofthevideoqualityi slittlelos tandtheoutpu tbi trateincreasei sveryl ittle.

t n e m e g d e l w o n k c A

s i h

T workispartiallysupportedbyt heNationa lNatura lScienceFoundationofChina(N .o61102131), n o i s s i m m o C y g o l o n h c e T d n a e c n e i c S l a p i c i n u M g n i q g n o h C f o n o i t a d n u o F e c n e i c S l a r u t a N l a n o i t a N

(stc2016jcyjA40027), Science and Technology Foundation of Chongqing Municipal and frontier t

c e j o r p h c r a e s e

r (cstc2017jcyjXB0037) .

s e c n e r e f e R

] 1

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