CN103702127B - Motion estimation search range Forecasting Methodology based on motion vector dependency and system - Google Patents

Abstract

The invention provides motion estimation search range Forecasting Methodology based on motion vector dependency and system, the method comprising the steps of: S1, presets maximum search scope SR_maxWith minimum search range SR_min；The fitting coefficient of predicting unit PU under S2, calculating different mode；S3, pattern according to current PU, obtain corresponding fitting coefficient；S4, pattern according to current PU, obtain corresponding motion vector predictor and available spatially and temporally correlation candidate motion vector；S5, calculation of motion vectors predict the cumulative difference of sub and each available spatially and temporally correlation candidate motion vector；S6, according to the fitting coefficient in S2, the motion vector predictor in S4, the cumulative difference in S5, calculate basic search scope；S7, according to basic search scope, maximum search scope SR set in advance_maxWith minimum search range SR_minDetermine final hunting zone SR_k.This invention can dynamically adjust hunting zone, thus reaches reduce amount of calculation and shorten the purpose of search time.

Description

Motion estimation search range Forecasting Methodology based on motion vector dependency and system

Technical field

The present invention relates to technical field of video processing, particularly relate to based on motion vector dependency Motion estimation search range Forecasting Methodology and system.

Background technology

Along with the development of technology, fine definition, that the video of high frame per second becomes people gradually is new Demand.High efficiency Video coding (HEVC, High Efficiency Video Coding) is by moving State motion picture expert group version (MPEG, Moving Pictures Experts Group) and international telecommunication connection Alliance's telecommunication standardsization tissue (ITU-T, ITU-T for ITU Telecommunication Standardization Sector) Video Coding Experts group (VCEG, Video Coding Experts Group) Video coding collectively constituted combines group (JCT-VC, Joint Collaborative Team On Video Coding) video compression standard of new generation developed jointly.HEVC is meant to ensure that and regards Frequently can be by Bit-Rate Reduction to previous generation video encoding standard on the premise of image quality does not declines H.264/MPEG-4AVC 50%.But, the cost that bit rate declines is then coding complexity Being increased sharply of degree.Can be used to reduce huge amount of calculation despite many methods, but Also there is a need to the work of many to further speed up the speed of coding.HEVC have employed code tree Unit (CTU, Coding Tree Unit) structure.Can comprise inside each CTU one or Multiple coding units (CU, Coding Unit), and each CU correspond to predicting unit (PU, Prediction Unit) and converter unit (TU, Transform Unit).Estimation (ME, Motion Estimation) time, current PU is by needing search in encoded reference frame The part mated most.

At present, the mode of PU Optimizing Search process is mainly in a fixing hunting zone Preferable position is obtained, the problem that this method exists by search:

1, the motion conditions of current PU itself is not accounted for, also cannot be according to current PU's Motion conditions reasonably limits the scope of search；

2, the concordance of the motion of current PU and CU around is not accounted for, also cannot be certainly The adjustment hunting zone adapted to；

Summary of the invention

For the deficiencies in the prior art, the present invention provides intraframe coding based on pattern pretreatment excellent Change method and system so that can stably accelerate coding rate in HEVC encoding-operation process.

For achieving the above object, the present invention is achieved by the following technical programs:

Motion estimation search range Forecasting Methodology based on motion vector dependency, including following step Rapid:

S1, preset maximum search scope SR_maxWith minimum search range SR_min；

The fitting coefficient ah of predicting unit PU under S2, calculating different mode_k,av_kAnd bh_k,bv_k, Wherein the pattern sum of predicting unit PU is Q, 0≤k < Q；

S3, pattern k according to current PU, obtain corresponding fitting coefficient ah_k,av_kAnd bh_k,bv_k；

S4, pattern k according to current PU, obtain corresponding motion vector predictor (MVPh_k,MVPv_k) and available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki), wherein, iCount_kRepresent available spatially and temporally correlation candidate motion Total number of vector, 0≤i < iCount_k；

S5, calculation of motion vectors prediction son (MVPh_k,MVPv_k) and each available spatially and temporally phase Close candidate motion vector (MVCh_ki,MVCv_ki) cumulative difference (Δ MVTh_k,ΔMVTv_k)；

S6, according to the fitting coefficient ah in S2_k,av_kAnd bh_k,bv_k, the motion vector in S4 is pre- Survey son (MVPh_k,MVPv_k), cumulative difference (the Δ MVTh in S5_k,ΔMVTv_k), calculate basic search scope SRBasic_k；

S7, according to basic search scope SRBasic in S6_kWith the maximum search scope in S1 SR_maxWith minimum search range SR_minDetermine final hunting zone SR_k。

Further, described step S2 includes:

S21, determine all patterns of predicting unit PU；

PU in S22, acquisition cycle tests_jkThe available candidate spatially and temporally moving relevant fortune Moving vector (MVCh_ijk,MVCv_ijk), 0≤k < Q, j represents jth PU under kth kind pattern, Q is the assemble mode number of predicting unit；

S23, acquisition PU_jkMotion vector predictor (MVPh_jk,MVPv_jk) and actual motion vector (MVh_jk,MVv_jk)；

S24, calculation of motion vectors prediction son (MVPh_jk,MVPv_jk) and each available spatial domain and time Territory correlation candidate motion vector (MVCh_ijk,MVCv_ijk) difference, after taking absolute value sue for peace:

${\mathrm{\&Delta;MVTh}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ijk}-{\mathrm{MVPh}}_{jk}|$

${\mathrm{\&Delta;MVTv}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ijk}-{\mathrm{MVPv}}_{jk}|$

Wherein, iCount_jkRepresent available spatially and temporally correlation candidate motion vector Number；

S25, calculating PU_jkActual motion vector (MVh_jk,MVv_jk) and motion vector predictor (MVPh_jk,MVPv_jk) difference, and take absolute value:

ΔMVh_jk=| MVh_jk-MVPh_jk|

ΔMVv_jk=| MVv_jk-MVPv_jk|

S26, structure following two equation group:

ah_k×|MVPh_jk|+bh_k×ΔMVTh_jk=Δ MVh_jk

av_k×|MVPv_jk|+bv_k×ΔMVTv_jk=Δ MVv_jk

Least square fitting algorithm is utilized to be calculated ah_k,av_kAnd bh_k,bv_k。

Further, described step S5 includes:

Calculation of motion vectors prediction son (MVPh_k,MVPv_k) and each available being spatially and temporally correlated with Candidate motion vector (MVCh_ki,MVCv_ki) difference, after taking absolute value sue for peace:

${\mathrm{\&Delta;MVTh}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ki}-{\mathrm{MVPh}}_k|$

${\mathrm{\&Delta;MVTv}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ki}-{\mathrm{MVPv}}_k|$

Wherein, iCount_kRepresent the number of available spatially and temporally correlation candidate motion vector.

Further, described step S6 includes:

According to the fitting coefficient ah in S2_k,av_kAnd bh_k,bv_k, motion vector predictor in S4 (MVPh_k,MVPv_k), cumulative difference (the Δ MVTh in S5_k,ΔMVTv_k), calculating basic search scope:

SRh_k-basic=| ah_k×ΔMVTh_k+bh_k×|MVPh_k||

SRv_k-basic=| av_k×ΔMVTv_k+bv_k×|MVPv_k||。

SRBasic_k=SRh_k-basic+SRv_k-basic

Further, described step S7 includes:

According to basic search scope SRBasic_kWith maximum search scope SR set in advance_max? Little hunting zone SR_minDetermine final hunting zone SR_kFor:

${\mathrm{SR}}_k=\left\{\begin{array}{cc}{\mathrm{SR}}_{\min },& {\mathrm{SRBasic}}_k<{\mathrm{SR}}_{\min }\\ {\mathrm{SRBasic}}_k,& {\mathrm{SR}}_{\min }\&le;{\mathrm{SRBasic}}_k\&le;{\mathrm{SR}}_{\max }\\ {\mathrm{SR}}_{\max },& {\mathrm{SRBasic}}_k>{\mathrm{SR}}_{\max }\end{array}\right..$

Motion estimation search range prognoses system based on motion vector dependency, this system includes:

Searched in advance range set module, is used for presetting maximum search scope SR_maxSearch with minimum Rope scope SR_min；

Fitting coefficient computing module, for calculating the fitting coefficient of predicting unit PU under different mode ah_k,av_kAnd bh_k,bv_k, wherein the pattern sum of predicting unit PU is Q, 0≤k < Q；

The fitting coefficient acquisition module of associative mode, for pattern k according to current PU, obtains Corresponding fitting coefficient ah_k,av_kAnd bh_k,bv_k；

Prediction of associative mode and associated motion vector acquisition module, for according to current PU's Pattern k, obtains corresponding motion vector predictor (MVPh_k,MVPv_k) and available spatial domain and time Territory correlation candidate motion vector (MVCh_ki,MVCv_ki), wherein, iCount_kRepresent available spatial domain and Total number of time domain correlation candidate motion vector, 0≤i < iCount_k；

Correlation calculations module, for calculation of motion vectors prediction son (MVPh_k,MVPv_k) and each can Spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki) cumulative difference (ΔMVTh_k,ΔMVTv_k)；

Basic search range computation module, for the fitting coefficient acquisition module according to associative mode In fitting coefficient ah_k,av_kAnd bh_k,bv_k, prediction of associative mode and associated motion vector obtain Motion vector predictor (MVPh in delivery block_k,MVPv_k), the cumulative difference in correlation calculations module (ΔMVTh_k,ΔMVTv_k), calculate basic search scope SRBasic_k；

Final hunting zone determines module, for according to the base in basic search range computation module This hunting zone SRBasic_kWith maximum search scope SR in searched in advance range set module_max With minimum search range SR_minDetermine final hunting zone SR_k。

Further, described fitting coefficient computing module includes:

PU pattern determining unit, for determining all patterns of predicting unit PU；

Associated motion vector acquiring unit, is used for obtaining PU in cycle tests_jkAvailable spatial domain and Candidate motion vector (the MVCh that temporal motion is relevant_ijk,MVCv_ijk), < Q, j represent kth to 0≤k Jth PU under the pattern of kind, k is the pattern of current prediction unit；

Prediction and actual motion vector acquiring unit, be used for obtaining PU_jkMotion vector predictor (MVPh_jk,MVPv_jk) and actual motion vector (MVh_jk,MVv_jk)；

Correlation calculations unit, for calculation of motion vectors prediction son (MVPh_jk,MVPv_jk) and each Individual available spatially and temporally correlation candidate motion vector (MVCh_ijk,MVCv_ijk) difference, take absolute value Rear summation:

${\mathrm{\&Delta;MVTh}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ijk}-{\mathrm{MVPh}}_{jk}|$

${\mathrm{\&Delta;MVTv}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ijk}-{\mathrm{MVPv}}_{jk}|$

Wherein, iCount_jkRepresent the number of available spatially and temporally correlation candidate motion vector；

Difference computational unit, is used for calculating PU_jkActual motion vector (MVh_jk,MVv_jk) and motion Vector predictor (MVPh_jk,MVPv_jk) difference, and take absolute value:

$\begin{array}{c}{\mathrm{\&Delta;MVh}}_{jk}=|{\mathrm{MVh}}_{jk}-{\mathrm{MVPh}}_{jk}|\\ {\mathrm{\&Delta;MVv}}_{jk}=|{\mathrm{MVv}}_{jk}-{\mathrm{MVPv}}_{jk}|\end{array};$

Coefficient Fitting unit, is used for constructing following two equation group:

ah_k×|MVPh_jk|+bh_k×ΔMVTh_jk=Δ MVh_jk

av_k×|MVPv_jk|+bv_k×ΔMVTv_jk=Δ MVv_jk

Least square fitting algorithm is utilized to be calculated ah_k,av_kAnd bh_k,bv_k。

Wherein, described correlation calculations module is for calculation of motion vectors prediction son (MVPh_k,MVPv_k) With each available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki) difference, take absolutely Sue for peace to after value:

${\mathrm{\&Delta;MVTh}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ki}-{\mathrm{MVPh}}_k|$

${\mathrm{\&Delta;MVTv}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ki}-{\mathrm{MVPv}}_k|$

Wherein, iCount_kRepresent the number of available spatially and temporally correlation candidate motion vector.

Further, described basic search range computation module is for the matching according to associative mode Fitting coefficient ah in coefficient acquisition module_k,av_kAnd bh_k,bv_k, prediction of associative mode and phase Close the motion vector predictor (MVPh in motion vector acquisition module_k,MVPv_k), correlation calculations mould Cumulative difference (Δ MVTh in block_k,ΔMVTv_k), calculate basic search scope SRBasic_k:

SRh_k-basic=| ah_k×ΔMVTh_k+bh_k×|MVPh_k||

SRv_k-basic=| av_k×ΔMVTv_k+bv_k×|MVPv_k||。

SRBasic_k=SRh_k-basic+SRv_k-basic

Further, described final hunting zone determines that module is for according to basic search scope SRBasic_kWith maximum search scope SR set in advance_maxWith minimum search range SR_minDetermine Whole hunting zone SR_k:

${\mathrm{SR}}_k=\left\{\begin{array}{cc}{\mathrm{SR}}_{\min },& {\mathrm{SRBasic}}_k<{\mathrm{SR}}_{\min }\\ {\mathrm{SRBasic}}_k,& {\mathrm{SR}}_{\min }\&le;{\mathrm{SRBasic}}_k\&le;{\mathrm{SR}}_{\max }\\ {\mathrm{SR}}_{\max },& {\mathrm{SRBasic}}_k>{\mathrm{SR}}_{\max }\end{array}\right..$

The present invention has a following beneficial effect:

The present invention is by utilizing the motion vector predictor (MVPh of current PU_k,MVPv_k), available Spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki), fitting coefficient ah_k,av_kWith bh_k,bv_kDynamically adjust basic search scope SRBasic_k, finally by maximum search model set in advance Enclose SR_max, minimum search range SR_minWith basic search scope SRBasic dynamically adjusted_kCommon true Fixed final hunting zone SR_k.Correlation candidate motion is spatially and temporally gone up owing to present invention utilizes The motion vector predictor of the current PU of vector sum combines the motion estimation search predicting current PU Scope, therefore, it is possible to adjust hunting zone real-time dynamicly according to practical situation, thus reduces meter Calculation amount, decreases search time simultaneously.

Accompanying drawing explanation

In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below The accompanying drawing used required in embodiment or description of the prior art will be briefly described, aobvious and Easily insight, the accompanying drawing in describing below is some embodiments of the present invention, common for this area From the point of view of technical staff, on the premise of not paying creative work, it is also possible to according to these accompanying drawings Obtain other accompanying drawing.

Fig. 1 is motion estimation search model based on motion vector dependency in the embodiment of the present invention 1 Enclose the flow chart of Forecasting Methodology；

Fig. 2 is motion estimation search model based on motion vector dependency in the embodiment of the present invention 2 Enclose the flow chart of Forecasting Methodology；

Fig. 3 is the relative of the candidate motion vector that the spatial domain described in the embodiment of the present invention 2 is relevant Position view；

Fig. 4 is the relative of the candidate motion vector that the time domain described in the embodiment of the present invention 2 is relevant Position view；

Fig. 5 is motion estimation search model based on motion vector dependency in the embodiment of the present invention 3 Enclose the structural representation of prognoses system.

Detailed description of the invention

For making the purpose of the embodiment of the present invention, technical scheme and advantage clearer, below will knot Close the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, Complete description, it is clear that described embodiment be a part of embodiment of the present invention rather than Whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art are not having Make the every other embodiment obtained under creative work premise, broadly fall into present invention protection Scope.

Embodiment 1

The embodiment of the present invention 1 proposes motion estimation search range based on motion vector dependency Forecasting Methodology, sees Fig. 1, comprises the steps:

Step 101: preset maximum search scope SR_maxWith minimum search range SR_min。

Step 102: calculate the fitting coefficient ah of predicting unit PU under different mode_k,av_kWith bh_k,bv_k, wherein the pattern sum of predicting unit PU is Q, 0≤k < Q.

Step 103: according to pattern k of current PU, obtain corresponding fitting coefficient ah_k,av_kWith bh_k,bv_k。

Step 104: according to pattern k of current PU, obtain corresponding motion vector predictor (MVPh_k,MVPv_k) and available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki), Wherein, iCount_kRepresent total number of available spatially and temporally correlation candidate motion vector, 0≤i<iCount_k。

Step 105: calculation of motion vectors prediction son (MVPh_k,MVPv_k) and each available spatial domain and Time domain correlation candidate motion vector (MVCh_ki,MVCv_ki) cumulative difference (Δ MVTh_k,ΔMVTv_k)。

Step 106: according to the fitting coefficient ah in step 102_k,av_kAnd bh_k,bv_k, step 104 In motion vector predictor (MVPh_k,MVPv_k), the cumulative difference in step 105 (ΔMVTh_k,ΔMVTv_k), calculate basic search scope SRBasic_k。

Step 107: according to basic search scope SRBasic_kWith maximum search scope set in advance SR_maxWith minimum search range SR_minDetermine final hunting zone SR_k。

Visible, the embodiment of the present invention is by utilizing the motion vector predictor of current PU (MVPh_k,MVPv_k), available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki), intend Syzygy number ah_k,av_kAnd bh_k,bv_kDynamically adjust basic search scope SRBasic_k, finally by setting in advance Fixed maximum search scope SR_max, minimum search range SR_minWith the basic search scope dynamically adjusted SRBasic_kJointly determine final hunting zone SR_k.To sum up describe visible, owing to the present invention is real Execute example to make use of and spatially and temporally go up correlation candidate motion vector and estimate to the motion predicting current PU Meter hunting zone, therefore, it is possible to adjust hunting zone real-time dynamicly according to practical situation, thus Reach to reduce the purpose of amount of calculation.

Embodiment 2

A preferred embodiment of the more detailed description present invention is carried out below by embodiment 2 Realize process.Seeing Fig. 2, this process comprises the steps:

Step 201: preset maximum search scope SR_maxWith minimum search range SR_min。

In this step, SR_maxValue be referred to the fixing search scope of prior art, example As, the fixing search scope before not using the present invention to be optimized is set to 64, then SR herein_maxIt is also configured as 64；Due to the hunting zone that likely obtains when being predicted very Little close to zero, if directly using the hunting zone of prediction, the result of search may be bad, Now use and limit a minimum hunting zone SR_min, can be by the SR limited_minIt is suitable to come Increase hunting zone and obtain a more stable result.General SR_minTake 4 be one proper Value.

Step 202: determine all patterns of predicting unit PU.

In this step, the mould of all different predicting unit (PU, Prediction Unit) is determined Formula: with reference to HM software, such as, for the CTU of 64x64, pattern Q=20 of PU, divides It is not:

4×8,4×16,4×8,8×8,8×16,

8×32,12×16,16×4,16×8,16×12,

16×16,16×32,24×32,32×8,32×16,

32×24,32×32,32×64,64×32,64×64。

Due to the motion conditions difference in the region that the PU of different mode is comprised, so for difference The PU of pattern uses different fitting coefficients to be obtained in that reasonable effect.

Step 203: obtain PU in cycle tests_jkAvailable relevant time of spatially and temporally moving Select motion vector (MVCh_ijk,MVCv_ijk), 0≤k < Q, j represents the jth under kth kind pattern PU, Q are the pattern sum of predicting unit.

In this step, suitable cycle tests is determined, it is thus achieved that different mode in cycle tests PU_jkAvailable candidate motion vector (the MVCh spatially and temporally moving relevant_ijk,MVCv_ijk), including Current PU_jkThe left side, spatial domain (L), lower-left (LD), top (U), upper right (RU) and a left side The motion vector (as shown in Figure 3) of upper (LU) 5 CU and time domain bottom right (RD) or The motion vector (as shown in Figure 4) of 1 CU in both centers (C), wherein time domain fortune In moving vector, the priority of bottom right is higher than center, and records relevant time of spatially and temporally moving Select the number iCount of motion vector_jk, wherein j represents jth PU, and 0≤k < Q represents The PU of k kind pattern.In implementation process, owing to PU location is different, actual available Number iCount of candidate motion vector_jkAlso different.Available candidate motion vector depends on after obtaining Secondary leaving in stores before position, can record the individual of final available candidate motion vector simultaneously Number iCount_jk；And disabled candidate motion vector is set to (0,0), leave storage position in Below.When calculating owing to simply using above iCount_jkIndividual candidate motion vector, does not use It is stored in candidate motion vector below, obtains several motion vectors, the party so actual Method can be correct process.

Step 204: obtain PU_jkMotion vector predictor (MVPh_jk,MVPv_jk) and actual motion to Amount (MVh_jk,MVv_jk)。

In this step, HEVC test model HM can centered by motion vector predictor, SR_maxIn the range of search obtain optimal matched position, this optimal position is exactly actual motion Vector.This step is the process of estimation in fact: before estimation only know motion to Amount prediction son, by motion vector predictor obtain reality motion vector, record this two Class value.

Step 205: calculation of motion vectors prediction son (MVPh_jk,MVPv_jk) and each available spatial domain With time domain correlation candidate motion vector (MVCh_ijk,MVCv_ijk) cumulative difference.

In this step, solve in the following way:

${\mathrm{\&Delta;MVTh}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ijk}-{\mathrm{MVPh}}_{jk}|$

${\mathrm{\&Delta;MVTv}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ijk}-{\mathrm{MVPv}}_{jk}|$

Step 206: calculate PU_jkActual motion vector (MVh_jk,MVv_jk) and motion vector predictor (MVPh_jk,MVPv_jk) difference, and take absolute value.

In this step, solve as follows:

ΔMVh_jk=| MVh_jk-MVPh_jk|

ΔMVv_jk=| MVv_jk-MVPv_jk|

Step 207: equationof structure group, utilizes least square fitting algorithm to obtain fitting coefficient ah_k,av_kAnd bh_k,bv_k。

In this step, structure following two equation group:

ah_k×|MVPh_jk|+bh_k×ΔMVTh_jk=Δ MVh_jk

av_k×|MVPv_jk|+bv_k×ΔMVTv_jk=Δ MVv_jk

Wherein k represents different PU patterns, and j represents all values belonging to pattern k, utilizes A young waiter in a wineshop or an inn takes advantage of fitting algorithm to be calculated ah_k,av_kAnd bh_k,bv_k。

Step 208: according to pattern k of current PU, obtain corresponding motion vector predictor (MVPh_k,MVPv_k) and available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki), Wherein, iCount_kRepresent total number of available spatially and temporally correlation candidate motion vector, 0≤i<iCount_k。

In this step, it is thus achieved that the motion vector predictor (MVPh of current PU_k,MVPv_k) and can Spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki), including current PU spatial domain The left side (L), lower-left (LD), top (U), upper right (RU) and upper left (LU) 5 Both the motion vector (as shown in Figure 3) of CU and time domain bottom right (RD) or center (C) In the motion vector (as shown in Figure 4) of 1 CU, wherein bottom right in temporal motion vector Priority higher than center, wherein i represent all available spatially and temporally correlation candidate motions to In amount one, and record the number iCount of motion vector_k；

Step 209: calculation of motion vectors prediction son (MVPh_k,MVPv_k) and each available spatial domain and Time domain correlation candidate motion vector (MVCh_ki,MVCv_ki) cumulative difference (Δ MVTh_k,ΔMVTv_k)。

In this step, cumulative poor (Δ MVTh is solved in such a way_k,ΔMVTv_k):

${\mathrm{\&Delta;MVTh}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ki}-{\mathrm{MVPh}}_k|$

${\mathrm{\&Delta;MVTv}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ki}-{\mathrm{MVPv}}_k|$

Step 210: according to the fitting coefficient ah in step 207_k,av_kAnd bh_k,bv_k, step 208 In motion vector predictor (MVPh_k,MVPv_k), the cumulative difference in step 209 (ΔMVTh_k,ΔMVTv_k), calculate basic search scope SRBasic_k。

In this step, basic search scope SRBasic is solved in such a way_k:

SRh_k-basic=| ah_k×ΔMVTh_k+bh_k×|MVPh_k||

SRv_k-basic=| av_k×ΔMVTv_k+bv_k×|MVPv_k||

SRBasic_k=SRh_k-basic+SRv_k-basic

(ΔMVTh_k,ΔMVTv_k) illustrate the motion phase of current PU and the most relevant CU Guan Xing: if two values are smaller, illustrates that current PU motion conditions is to the most relevant The ratio of CU more consistent, hunting zone should be less；Otherwise, they motion conditions are described not Unanimously, hunting zone should become big.(MVPh_k,MVPv_k) illustrate motion feelings possible for current PU Condition: two values are smaller, illustrates that the motion ratio of current PU itself is shallower or background is without motion, The reduction that so hunting zone can be suitable；Otherwise, illustrate that the motion ratio of current PU itself is more acute Strong, then hunting zone should be bigger.

Step 211: according to basic search scope SRBasic_kWith maximum search scope set in advance SR_maxWith minimum search range SR_minDetermine final hunting zone SR_k。

In this step, carry out in such a way solving final hunting zone SR_k:

${\mathrm{SR}}_k=\left\{\begin{array}{cc}{\mathrm{SR}}_{\min },& {\mathrm{SRBasic}}_k<{\mathrm{SR}}_{\min }\\ {\mathrm{SRBasic}}_k,& {\mathrm{SR}}_{\min }\&le;{\mathrm{SRBasic}}_k\&le;{\mathrm{SR}}_{\max }\\ {\mathrm{SR}}_{\max },& {\mathrm{SRBasic}}_k>{\mathrm{SR}}_{\max }\end{array}\right.$

Size by real-time adjustment SR, it is possible to the effective time reducing integral point search. Test result shows, the method can reduce by the integral point search time of 20% to 55%, simultaneously Ensure that the distortion rate of signal is at below 0.08dB.

The embodiment of the present invention is by utilizing the motion vector predictor (MVPh of current PU_k,MVPv_k)、 Available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki), fitting coefficient ah_k,av_k And bh_k,bv_kDynamically adjust basic search scope SRBasic_k, finally by maximum search set in advance Scope SR_max, minimum search range SR_minWith basic search scope SRBasic dynamically adjusted_kJointly Determine final hunting zone SR_k.To sum up describe visible, owing to the embodiment of the present invention make use of sky On territory and time domain, correlation candidate motion vector predicts the motion estimation search range of current PU, because of This can adjust hunting zone real-time dynamicly according to practical situation, thus has reached to reduce calculating Amount and the purpose shortening search time.

Embodiment 3

The embodiment of the present invention 3 also proposed motion estimation search model based on motion vector dependency Enclosing prognoses system, see Fig. 5, this system includes:

Hunting zone presets module 501, is used for presetting maximum search scope SR_max? Little hunting zone SR_min；

Fitting coefficient computing module 502, for calculating the matching of predicting unit PU under different mode Coefficient ah_k,av_kAnd bh_k,bv_k, wherein the pattern sum of predicting unit PU is Q, 0≤k < Q；

The fitting coefficient acquisition module 503 of associative mode, for pattern k according to current PU, Obtain corresponding fitting coefficient ah_k,av_kAnd bh_k,bv_k；

Prediction of associative mode and associated motion vector acquisition module 504, for according to current Pattern k of PU, obtains corresponding motion vector predictor (MVPh_k,MVPv_k) and available sky Territory and time domain correlation candidate motion vector (MVCh_ki,MVCv_ki), wherein, iCount_kExpression can use Spatially and temporally total number of correlation candidate motion vector, 0≤i < iCount_k；

Correlation calculations module 505, for calculation of motion vectors prediction son (MVPh_k,MVPv_k) and every Individual available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki) cumulative difference (ΔMVTh_k,ΔMVTv_k)；

Basic search range computation module 506, obtains mould for the fitting coefficient according to associative mode Fitting coefficient ah in block_k,av_kAnd bh_k,bv_k, prediction of associative mode and associated motion vector Motion vector predictor (MVPh in acquisition module_k,MVPv_k), adding up in correlation calculations module Difference (Δ MVTh_k,ΔMVTv_k), calculate basic search scope SRBasic_k；

Final hunting zone determines module 507, for according in basic search range computation module Basic search scope SRBasic_kWith the maximum search scope in searched in advance range set module SR_maxWith minimum search range SR_minDetermine final hunting zone SR_k。

Wherein, described fitting coefficient computing module 502 includes:

PU pattern determining unit 5020, for determining all patterns of predicting unit PU:

In this unit, with reference to HM software, such as, for the CTU of 64x64, PU's Pattern Q=20, is respectively as follows:

4×8,4×16,4×8,8×8,8×16,

8×32,12×16,16×4,16×8,16×12,

16×16,16×32,24×32,32×8,32×16,

32×24,32×32,32×64,64×32,64×64。

Due to the motion conditions difference in the region that the PU of different mode is comprised, so for difference The PU of pattern uses different fitting coefficients to be obtained in that reasonable effect.

Associated motion vector acquiring unit 5021, is used for obtaining PU in cycle tests_jkAvailable Candidate motion vector (the MVCh spatially and temporally moving relevant_ijk,MVCv_ijk), 0≤k < Q, j Representing jth PU under kth kind pattern, Q is the assemble mode number of predicting unit；

In this unit, it is first determined suitably cycle tests, then in acquisition cycle tests not PU with pattern_jkThe available relevant candidate motion vector that spatially and temporally moves (MVCh_ijk,MVCv_ijk), including current PU_jkThe left side, spatial domain (L), lower-left (LD), top (U), The motion vector (as shown in Figure 3) of upper right (RU) and upper left (LU) 5 CU and The motion vector of 1 CU in both time domain bottom right (RD) or center (C) is (such as Fig. 4 Shown in), wherein in temporal motion vector the priority of bottom right higher than center, and record spatial domain and The number iCount of the candidate motion vector that temporal motion is relevant_jk, wherein j represents jth PU, 0≤k < Q represents the PU of kth kind pattern.In implementation process, due to PU location Difference, number iCount of actual available candidate motion vector_jkAlso different.Available candidate's fortune Moving vector leaves in before storage position after obtaining successively, can record final available time simultaneously Select number iCount of motion vector_jk；And disabled candidate motion vector is set to (0,0), Leave in after storage position.When calculating owing to simply using above iCount_jkIndividual candidate transports Moving vector, does not has to use the candidate motion vector being stored in below, so actual acquisition is several Individual motion vector, the process that the method can be correct.

Prediction and actual motion vector acquiring unit 5022, be used for obtaining PU_jkMotion vector Prediction son (MVPh_jk,MVPv_jk) and actual motion vector (MVh_jk,MVv_jk)；

Correlation calculations unit 5023, for calculation of motion vectors prediction son (MVPh_jk,MVPv_jk) and Each available spatially and temporally correlation candidate motion vector (MVCh_ijk,MVCv_ijk) difference, take absolutely Sue for peace to after value:

${\mathrm{\&Delta;MVTh}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ijk}-{\mathrm{MVPh}}_{jk}|$

${\mathrm{\&Delta;MVTv}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ijk}-{\mathrm{MVPv}}_{jk}|$

Wherein, iCount_jkRepresent the number of available spatially and temporally correlation candidate motion vector；

Difference computational unit 5024, is used for calculating PU_jkActual motion vector (MVh_jk,MVv_jk) and Motion vector predictor (MVPh_jk,MVPv_jk) difference, and take absolute value:

$\begin{array}{c}{\mathrm{\&Delta;MVh}}_{jk}=|{\mathrm{MVh}}_{jk}-{\mathrm{MVPh}}_{jk}|\\ {\mathrm{\&Delta;MVv}}_{jk}=|{\mathrm{MVv}}_{jk}-{\mathrm{MVPv}}_{jk}|\end{array};$

Coefficient Fitting unit 5025, is used for constructing following two equation group:

ah_k×|MVPh_jk|+bh_k×ΔMVTh_jk=Δ MVh_jk

av_k×|MVPv_jk|+bv_k×ΔMVTv_jk=Δ MVv_jk

Least square fitting algorithm is utilized to be calculated ah_k,av_kAnd bh_k,bv_k。

Wherein, described correlation calculations module 505 is for calculation of motion vectors prediction (MVPh_k,MVPv_k) and each available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki) difference, after taking absolute value sue for peace:

${\mathrm{\&Delta;MVTh}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ki}-{\mathrm{MVPh}}_k|$

${\mathrm{\&Delta;MVTv}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ki}-{\mathrm{MVPv}}_k|$

Wherein, iCount_kRepresent the number of available spatially and temporally correlation candidate motion vector.

Wherein, described basic search range computation module 506 is for the matching system according to associative mode Fitting coefficient ah in number acquisition module_k,av_kAnd bh_k,bv_k, prediction of associative mode is with relevant Motion vector predictor (MVPh in motion vector acquisition module_k,MVPv_k), correlation calculations module In cumulative difference (Δ MVTh_k,ΔMVTv_k), calculate basic search scope SRBasic_k:

SRh_k-basic=| ah_k×ΔMVTh_k+bh_k×|MVPh_k||

SRv_k-basic=| av_k×ΔMVTv_k+bv_k×|MVPv_k||。

SRBasic_k=SRh_k-basic+SRv_k-basic

Wherein, described final hunting zone determines that module 507 is for according to basic search scope SRBasic_kWith maximum search scope SR set in advance_maxWith minimum search range SR_minDetermine Whole hunting zone SR_k:

${\mathrm{SR}}_k=\left\{\begin{array}{cc}{\mathrm{SR}}_{\min },& {\mathrm{SRBasic}}_k<{\mathrm{SR}}_{\min }\\ {\mathrm{SRBasic}}_k,& {\mathrm{SR}}_{\min }\&le;{\mathrm{SRBasic}}_k\&le;{\mathrm{SR}}_{\max }\\ {\mathrm{SR}}_{\max },& {\mathrm{SRBasic}}_k>{\mathrm{SR}}_{\max }\end{array}\right..$

System described in the embodiment of the present invention, utilizes the motion vector predictor of current PU (MVPh_k,MVPv_k), available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki), intend Syzygy number ah_k,av_kAnd bh_k,bv_kDynamically adjust basic search scope SRBasic_k, finally by setting in advance Fixed maximum search scope SR_max, minimum search range SR_minWith the basic search scope dynamically adjusted SRBasic_kJointly determine final hunting zone SR_k.To sum up describe visible, owing to make use of sky On territory and time domain, correlation candidate motion vector predicts the motion estimation search range of current PU, because of This can adjust hunting zone real-time dynamicly according to practical situation, thus reduces amount of calculation, with Time also imply that and decrease search time.

Above example is merely to illustrate technical scheme, is not intended to limit；Although ginseng Being described in detail the present invention according to previous embodiment, those of ordinary skill in the art should Work as understanding: the technical scheme described in foregoing embodiments still can be modified by it, or Person carries out equivalent to wherein portion of techniques feature；And these amendments or replacement, do not make phase The essence answering technical scheme departs from the spirit and scope of various embodiments of the present invention technical scheme.

Claims (10)

1. motion estimation search range Forecasting Methodology based on motion vector dependency, its feature exists In, comprise the following steps:

S1, preset maximum search scope SR_maxWith minimum search range SR_min；

The fitting coefficient ah of predicting unit PU under S2, calculating different mode_k,av_kAnd bh_k,bv_k, Wherein the pattern sum of predicting unit PU is Q, 0≤k < Q；

S3, pattern k according to current PU, obtain corresponding fitting coefficient ah_k,av_kAnd bh_k,bv_k；

S4, pattern k according to current PU, obtain corresponding motion vector predictor (MVPh_k,MVPv_k) and available spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki), wherein, iCount_kRepresent available spatially and temporally correlation candidate motion Total number of vector, 0≤i < iCount_k；

S5, calculation of motion vectors prediction son (MVPh_k,MVPv_k) and each available spatially and temporally phase Close candidate motion vector (MVCh_ki,MVCv_ki) cumulative difference (Δ MVTh_k,ΔMVTv_k)；

S6, according to the fitting coefficient ah in S2_k,av_kAnd bh_k,bv_k, the motion vector in S4 is pre- Survey son (MVPh_k,MVPv_k), cumulative difference (the Δ MVTh in S5_k,ΔMVTv_k), calculate basic search scope SRBasic_k；

S7, according to basic search scope SRBasic in S6_kWith the maximum search scope in S1 SR_maxWith minimum search range SR_minDetermine final hunting zone SR_k。

Method the most according to claim 1, it is characterised in that described step S2 includes:

S21, determine all patterns of predicting unit PU；

PU in S22, acquisition cycle tests_jkThe available candidate spatially and temporally moving relevant fortune Moving vector (MVCh_ijk,MVCv_ijk), 0≤k < Q, j represents jth PU under kth kind pattern, Q is the assemble mode number of predicting unit；

S23, acquisition PU_jkMotion vector predictor (MVPh_jk,MVPv_jk) and actual motion vector (MVh_jk,MVv_jk)；

S24, calculation of motion vectors prediction son (MVPh_jk,MVPv_jk) and each available spatial domain and time Territory correlation candidate motion vector (MVCh_ijk,MVCv_ijk) difference, after taking absolute value sue for peace:

${\mathrm{\&Delta;MVTh}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ijk}-{\mathrm{MVPh}}_{jk}|$

${\mathrm{\&Delta;MVTv}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ijk}-{\mathrm{MVPv}}_{jk}|$

Wherein, iCount_jkRepresent available spatially and temporally correlation candidate motion vector Number；

S25, calculating PU_jkActual motion vector (MVh_jk,MVv_jk) and motion vector predictor (MVPh_jk,MVPv_jk) difference, and take absolute value:

ΔMVh_jk=| MVh_jk-MVPh_jk|

ΔMVv_jk=| MVv_jk-MVPv_jk|

S26, structure following two equation group:

ah_k×|MVPh_jk|+bh_k×ΔMVTh_jk=Δ MVh_jk

av_k×|MVPv_jk|+bv_k×ΔMVTv_jk=Δ MVv_jk

Least square fitting algorithm is utilized to be calculated ah_k,av_kAnd bh_k,bv_k。

Method the most according to claim 1, it is characterised in that described step S5 includes:

Calculation of motion vectors prediction son (MVPh_k,MVPv_k) and each available being spatially and temporally correlated with Candidate motion vector (MVCh_ki,MVCv_ki) difference, after taking absolute value sue for peace:

${\mathrm{\&Delta;MVTh}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ki}-{\mathrm{MVPh}}_k|$

${\mathrm{\&Delta;MVTv}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ki}-{\mathrm{MVPv}}_k|$

Wherein, iCount_kRepresent the number of available spatially and temporally correlation candidate motion vector.

Method the most according to claim 1, it is characterised in that described step S6 includes:

According to the fitting coefficient ah in S2_k,av_kAnd bh_k,bv_k, motion vector predictor in S4 (MVPh_k,MVPv_k), cumulative difference (the Δ MVTh in S5_k,ΔMVTv_k), calculating basic search scope:

SRh_k-basic=| ah_k×ΔMVTh_k+bh_k×|MVPh_k||

SRv_k-basic=| av_k×ΔMVTv_k+bv_k×|MVPv_k||。

SRBasic_k=SRh_k-basic+SRv_k-basic

Method the most according to claim 1, it is characterised in that described step S7 includes:

According to basic search scope SRBasic_kWith maximum search scope SR set in advance_max? Little hunting zone SR_minDetermine final hunting zone SR_kFor:

${\mathrm{SR}}_k=\left\{\begin{array}{cc}{\mathrm{SR}}_{\min },& {\mathrm{SRBasic}}_k<{\mathrm{SR}}_{\min }\\ {\mathrm{SRBasic}}_k,& {\mathrm{SR}}_{\min }\&le;{\mathrm{SRBasic}}_k\&le;{\mathrm{SR}}_{\max }\\ {\mathrm{SR}}_{\max },& {\mathrm{SRBasic}}_k>{\mathrm{SR}}_{\max }\end{array}\right..$

6. motion estimation search range prognoses system based on motion vector dependency, its feature exists In, this system includes:

Searched in advance range set module, is used for presetting maximum search scope SR_maxSearch with minimum Rope scope SR_min；

Fitting coefficient computing module, for calculating the fitting coefficient of predicting unit PU under different mode ah_k,av_kAnd bh_k,bv_k, wherein the pattern sum of predicting unit PU is Q, 0≤k < Q；

The fitting coefficient acquisition module of associative mode, for pattern k according to current PU, obtains Corresponding fitting coefficient ah_k,av_kAnd bh_k,bv_k；

Prediction of associative mode and associated motion vector acquisition module, for according to current PU's Pattern k, obtains corresponding motion vector predictor (MVPh_k,MVPv_k) and available spatial domain and time Territory correlation candidate motion vector (MVCh_ki,MVCv_ki), wherein, iCount_kRepresent available spatial domain and Total number of time domain correlation candidate motion vector, 0≤i < iCount_k；

Correlation calculations module, for calculation of motion vectors prediction son (MVPh_k,MVPv_k) and each can Spatially and temporally correlation candidate motion vector (MVCh_ki,MVCv_ki) cumulative difference (ΔMVTh_k,ΔMVTv_k)；

Basic search range computation module, for the fitting coefficient acquisition module according to associative mode In fitting coefficient ah_k,av_kAnd bh_k,bv_k, prediction of associative mode and associated motion vector obtain Motion vector predictor (MVPh in delivery block_k,MVPv_k), the cumulative difference in correlation calculations module (ΔMVTh_k,ΔMVTv_k), calculate basic search scope SRBasic_k；

Final hunting zone determines module, for according to the base in basic search range computation module This hunting zone SRBasic_kWith maximum search scope SR in searched in advance range set module_max With minimum search range SR_minDetermine final hunting zone SR_k。

System the most according to claim 6, it is characterised in that described fitting coefficient calculates Module includes:

PU pattern determining unit, for determining all patterns of predicting unit PU；

Associated motion vector acquiring unit, is used for obtaining PU in cycle tests_jkAvailable spatial domain and Candidate motion vector (the MVCh that temporal motion is relevant_ijk,MVCv_ijk), < Q, j represent kth to 0≤k Jth PU under the pattern of kind, k is the pattern of current prediction unit；

Prediction and actual motion vector acquiring unit, be used for obtaining PU_jkMotion vector predictor (MVPh_jk,MVPv_jk) and actual motion vector (MVh_jk,MVv_jk)；

Correlation calculations unit, for calculation of motion vectors prediction son (MVPh_jk,MVPv_jk) and each Individual available spatially and temporally correlation candidate motion vector (MVCh_ijk,MVCv_ijk) difference, take absolute value Rear summation:

${\mathrm{\&Delta;MVTh}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ijk}-{\mathrm{MVPh}}_{jk}|$

${\mathrm{\&Delta;MVTv}}_{jk}=\underset{i=0}{\overset{{\mathrm{iCount}}_{jk}-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ijk}-{\mathrm{MVPv}}_{jk}|$

Wherein, iCount_jkRepresent the number of available spatially and temporally correlation candidate motion vector；

Difference computational unit, is used for calculating PU_jkActual motion vector (MVh_jk,MVv_jk) and motion Vector predictor (MVPh_jk,MVPv_jk) difference, and take absolute value:

ΔMVh_jk=| MVh_jk-MVPh_jk|；

ΔMVv_jk=| MVv_jk-MVPv_jk|

Coefficient Fitting unit, is used for constructing following two equation group:

ah_k×|MVPh_jk|+bh_k×ΔMVTh_jk=Δ MVh_jk

av_k×|MVPv_jk|+bv_k×ΔMVTv_jk=Δ MVv_jk

Least square fitting algorithm is utilized to be calculated ah_k,av_kAnd bh_k,bv_k。

System the most according to claim 6, it is characterised in that described correlation calculations mould Block is for calculation of motion vectors prediction son (MVPh_k,MVPv_k) and each available spatially and temporally phase Close candidate motion vector (MVCh_ki,MVCv_ki) difference, after taking absolute value sue for peace:

${\mathrm{\&Delta;MVTh}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCh}}_{ki}-{\mathrm{MVPh}}_k|$

${\mathrm{\&Delta;MVTv}}_k=\underset{i=0}{\overset{{\mathrm{iCount}}_k-1}{\&Sigma;}}|{\mathrm{MVCv}}_{ki}-{\mathrm{MVPv}}_k|$

Wherein, iCount_kRepresent the number of available spatially and temporally correlation candidate motion vector.

System the most according to claim 6, it is characterised in that described basic search scope Computing module is for according to the fitting coefficient ah in the fitting coefficient acquisition module of associative mode_k,av_k And bh_k,bv_k, prediction and the motion vector in associated motion vector acquisition module of associative mode Prediction son (MVPh_k,MVPv_k), cumulative difference (the Δ MVTh in correlation calculations module_k,ΔMVTv_k), meter Calculate basic search scope SRBasic_k:

SRh_k-basic=| ah_k×ΔMVTh_k+bh_k×|MVPh_k||

SRv_k-basic=| av_k×ΔMVTv_k+bv_k×|MVPv_k||。

SRBasic_k=SRh_k-basic+SRv_k-basic

System the most according to claim 6, it is characterised in that described final search model Enclose and determine that module is for according to basic search scope SRBasic_kWith maximum search model set in advance Enclose SR_maxWith minimum search range SR_minDetermine final hunting zone SR_k:

${\mathrm{SR}}_k=\left\{\begin{array}{cc}{\mathrm{SR}}_{\min },& {\mathrm{SRBasic}}_k<{\mathrm{SR}}_{\min }\\ {\mathrm{SRBasic}}_k,& {\mathrm{SR}}_{\min }\&le;{\mathrm{SRBasic}}_k\&le;{\mathrm{SR}}_{\max }\\ {\mathrm{SR}}_{\max },& {\mathrm{SRBasic}}_k>{\mathrm{SR}}_{\max }\end{array}\right..$