CN108200431B - Bit allocation method for video coding code rate control frame layer - Google Patents

Bit allocation method for video coding code rate control frame layer Download PDF

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CN108200431B
CN108200431B CN201711297946.9A CN201711297946A CN108200431B CN 108200431 B CN108200431 B CN 108200431B CN 201711297946 A CN201711297946 A CN 201711297946A CN 108200431 B CN108200431 B CN 108200431B
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CN108200431A (en
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李强
许鹤
明艳
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/146—Data rate or code amount at the encoder output
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/172—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/184—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being bits, e.g. of the compressed video stream
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/70—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards

Abstract

The invention requests to protect a bit distribution method of a video coding rate control frame layer, which comprises the following steps: on one hand, the synthesis factor is obtained by weighting and combining the image information entropy, the sum of the absolute errors of the minimum transform domain and the frame layer fixed weight in the R-lambda model code rate control algorithm. Then, measuring the complexity of the frame layer image by utilizing the comprehensive factor; on the other hand, the state of the video coding buffer is analyzed, and the bit allocation of the coding frame layer is adaptively adjusted through the feedback bit of the buffer obtained by calculation. The invention ensures that the bit distribution is more reasonable, not only can improve the precision of code rate control, but also can improve the quality of video coding.

Description

Bit allocation method for video coding code rate control frame layer
Technical Field
The invention belongs to the field of video coding and decoding, and particularly relates to a bit allocation method for a video coding rate control frame layer.
Background
70% of the information of a human being is captured by the eyes, so it is said that the smell of a person is not as good as one. Human vision can directly reflect the change of the environment. With the development and application of modern information processing technology and digital storage technology, video has become one of the efficient media for transmitting, recording and reproducing information. In order to meet the demand of high-quality video consumption, digital video has been shifted from 720P to 1080P, even 4K × 2K level, and 8K × 4K level video technology is under development. The frame rate of digital video has been increased from 30fps to 60fps and 120fps, and has even stepped toward the 240fps target. Since high definition and high frame rate videos contain a huge amount of information, the amount of data required to describe these videos is also very large and video traffic exhibits explosive growth. Because a large amount of information such as spatial redundancy, temporal redundancy, statistical redundancy, visual redundancy, structural redundancy, knowledge redundancy, image redundancy and the like exists in the video, if the redundant information can be removed by adopting the modes of prediction, transformation, quantization, entropy coding and the like, the compression coding of the video data can be realized.
Since high-performance video compression coding is one of the key technologies for relieving the pressure of the continuously increasing video data on the transmission and storage of communication networks, the video coding technology has been rapidly developed in recent decades, and domestic and international standardization organizations have successively established a number of international video coding standards. ITU-T and ISO have jointly released a new generation of international Video Coding standard, High Efficiency Video Coding (HEVC), in 2013 in 1 month. HEVC, like most video coding standards, also employs a hybrid coding framework of prediction plus transform, and fig. 1 is a coding framework of the HEVC standard. Because a new coding technology, such as high-precision motion compensation, motion estimation merging, semantic-based entropy coding, adaptive loop filtering and other new technologies, is introduced into each video coding module, the coding efficiency of the HEVC is greatly improved.
In video communication, because transmission resources in a communication network are limited, if the bit rate of an output code stream of a video encoder is too large, an output buffer area of a sending end is overflowed, so that video transmission is delayed or frames are lost, and when a receiving end decodes and plays, a video is interrupted or discontinuous; if the video compression ratio is large, the bit rate of the output code stream of the encoding end is small, the transmission channel resource can not be fully utilized, the quality of the decoded video is reduced, and the block effect or the edge contour blurring phenomenon can occur in the decoded video. The main means to solve the above problem is to use a rate control technique in the video encoder. The code rate control can not only improve the utilization rate of a channel, but also ensure higher video coding quality, so that the code rate control algorithm has important significance for the application of video coding and the normal communication transmission of video, and is an indispensable important component of the current video encoder.
The rate control procedure can be divided into two steps. The first step is to allocate an appropriate number Of bits to the coding units Of each layer, typically including a Group Of Pictures (GOP) layer, a frame layer, and a base coding unit layer. The encoder allocates an appropriate number of bits to each layer based on the buffer occupancy. The second step is to reach the bit number pre-allocated to each layer by determining the quantization parameter of the encoding end.
A good Rate control algorithm can accurately achieve the target Rate while minimizing the coding Distortion, so the Rate control problem is transformed into a Rate Distortion Optimization (RDO) problem as shown in formula (1), that is, the encoder selects the parameter that minimizes the Distortion as the optimal coding parameter when the number of coded bits does not exceed the target number of bits. { Para } in formula (1) represents a set of encoding parameters including mode, motion information, prediction parameters, quantization parameters, and the like; λ is the absolute value of the slope of the Lagrangian multiplier, i.e., the R-D (Rate-Distoretion) curve.
Figure BDA0001500679050000021
To improve the flexibility of encoding, the encoder can freely select various combinations of encoding parameters. Since selecting different parameters directly affects the coding bit rate of the final video, the rate control algorithm achieves the target rate by selecting appropriate coding parameters from the set of coding parameters.
HEVC adopts a novel lambda-domain code rate control algorithm based on an R-lambda model, accurately describes an R-D code rate distortion model in a coding algorithm by using a hyperbolic model, and obtains distortion of a video after compression coding through calculation of a formula (2), wherein R represents the bit rate after compression and is represented by bits (bpp) consumed by each pixel; c and K are model parameters related to the content characteristics of the video sequence, and the values of C and K are different from one video sequence to another.
D(R)＝CR-K (2)
The code rate control is to establish a mathematical relation between the code rate R and a Lagrange multiplier lambda used for coding on the basis of an R-D code rate distortion model, and to achieve the expected target code rate by using a lambda regulation method. Formula (3) can calculate the lagrange multiplier λ, where α ═ CK and β ═ K-1. The two parameters α and β are related to the content characteristics of the video sequence, with different sequences having different values. The relation between the code rate R and the lambda is further obtained by the formula (3), as shown in the formula (4).
Figure BDA0001500679050000031
Figure BDA0001500679050000032
As shown in equation (4), the code rate R is completely determined by the lagrange multiplier λ. The relationship between lambda and R-D curve is shown in FIG. 2.λ is the absolute value of the slope of the R-D curve determined by the convex envelope of all the actual operating points. There is a one-to-one correspondence between the code rate R and the lagrange multiplier λ. Since the R-D curve is a convex function, the minimization formula (1) calculated based on a certain λ value is equivalent to approximating the R-D curve using a straight line whose slope absolute value is the λ value, which is tangent to the R-D curve only at one point. Therefore, the λ value can directly determine the bitrate R and the video distortion D.
To achieve some assigned target code rate R, the encoder will determine the associated λ value according to equation (1) and use it in the encoding process. When the lambda value used for encoding is determined, all other encoding parameters are obtained by rate-distortion optimization.
The bit allocation of HEVC rate control is performed at three levels, the group of pictures (GOP) level, the frame level, and the basic coding unit level. And distributing the target bit number of the frame layer according to the fixed weight of the frame layer in the R-lambda model code rate control algorithm and a formula (5).
Figure BDA0001500679050000033
Wherein, TCurrPicIs the number of allocated bits, T, of the current frameGOPIs the number of allocated bits, Coded, of the current group of pictures (GOP)GOPRepresents the number of coded bits in the GOP,
Figure BDA0001500679050000041
representing the frame layer fixed weight of the current image frame in the R-lambda model rate control algorithm,
Figure BDA0001500679050000042
weights are assigned to the bits of all the unencoded pictures in the GOP.
As can be seen from the above, since the target bits of each frame in the group of pictures are determined in advance according to the corresponding coding structure and coding order and are fixed weight values, the complexity of the pictures of the video sequence itself and the state of the buffer are not considered, and thus the bit allocation is not reasonable.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A bit distribution method for a video coding rate control frame layer with reasonable bit distribution is provided. The technical scheme of the invention is as follows:
a method for distributing bits of a video coding rate control frame layer comprises the following specific steps:
step 1, inputting a video, and analyzing frame layer image textures of the video to obtain an image information entropy EI and a SATD value of the sum of absolute errors of a minimum transform domain;
step 2, according to the obtained EI and SATD values and the frame layer fixed weight in the R-lambda model code rate control algorithm
Figure BDA0001500679050000043
Calculating the bit distribution weight omega of each frame imagepic；
Step 3, calculating the bit number distributed by the frame layer according to the formula (1);
Figure BDA0001500679050000044
wherein, TCurrPicIs the number of bits allocated for the current frame; t isGOPIs the bit number allocated by the current group of pictures GOP; codedGOPRepresenting the number of coded bits in the GOP; omegapicDistributing weight to the bit of the current frame;
Figure BDA0001500679050000045
weights are assigned to the bits of all the unencoded pictures in the GOP.
Step 4, analyzing the state of the video coding buffer area, and calculating the feedback bit of the buffer area by setting the fullness degree of the buffer area;
step 5, according to the frame layer distribution bit number T calculated in step 3CurrPicAnd feedback bit delta of bufferTAnd calculating the bit number to be allocated to the current frame.
Further, the image information entropy EI in step 1 is calculated by using formula (2).
Figure BDA0001500679050000051
Where p (x) is the probability of the occurrence of the gray level of x of the image and N is the maximum gray level of the image.
Further, the sum of absolute errors SATD of the minimum transform domain in step 1 is calculated by formula (3).
Figure BDA0001500679050000052
Where M is the number of pixels in the rows and columns of the pixel block, hi,jThe pixel blocks are the corresponding values after Hadamard transformation.
Further, the frame layer bits in step 2 are assigned weights ωpicCalculated from equation (4).
Figure BDA0001500679050000053
Wherein, EIiIs the information entropy of the current frame image; SATDiThe sum of the minimum transform domain absolute errors of the current frame image is obtained;
Figure BDA0001500679050000054
representing the frame layer fixed weight of the current frame image in an R-lambda model code rate control algorithm; a and b are weighting coefficients, and the two coefficient values are more than 0 and less than 1.
Further, the feedback bit calculation process of the buffer in step 4 is as follows:
and calculating the target bit number obtained by distributing the coded image frame, calculating the bit number consumed in the actual coding process of the coded image frame, solving the sum of absolute differences of the two bit numbers, and then calculating the residual bit number of the buffer area through a formula (5).
Figure BDA0001500679050000055
Wherein, TbufleftIs the number of remaining bits in the buffer; i represents the sequence number of the encoded frame; n is the serial number of the current frame to be coded;
Figure BDA0001500679050000056
the number of bits allocated to the coded image frame in the current group of pictures GOP;
Figure BDA0001500679050000057
is the number of bits actually consumed by the coded pictures in the current group of pictures; to avoid buffer overflow, before encoding an image frame, a target buffer level L is set, so that the fullness of the buffer after encoding approaches the value as much as possible, and the calculation formula of the target buffer level L is shown in (6).
L＝μ×Bd (6)
Bd＝R/f (7)
Wherein the coefficient mu is a value between 0 and 1; b isdIs the size of the buffer; r is the channel rate, and the value can be preset in a configuration file; f is the frame rate.
The calculation formula of the feedback bits of the buffer is shown in (8).
ΔT＝η×(L-Tbufleft) (8)
Wherein, DeltaTFeedback bits representing a buffer; the coefficient eta is a value between 0 and 1; l is a target buffer level; t isbufleftIndicating the number of remaining bits in the buffer.
Bit number T based on buffer fullness allocationCurrPicbufCalculated by using the formula (9).
Figure BDA0001500679050000061
Further, in the step 5, a final frame layer bit allocation formula is obtained by adopting a method of weighted average of allocation weights based on the synthesis factor and buffer feedback bits, as shown in (10).
TCurrPicfal＝γ×TCurrPic+(1-γ)×TCurrPicbuf (10)
Wherein, TCurrPicfalRepresenting the finally allocated bit number of the current frame; t isCurrPicThe bit number is distributed to the current frame according to the distribution weight based on the comprehensive factor; t isCurrPicbufIs the number of bits allocated based on buffer fullness; and gamma is a weighting coefficient, the value range of gamma is 0-1, and the value is selected according to different configuration files.
Simultaneous equations (1), (9) and (10) give T as shown in (11)CurrPicfalThe calculation formula of (2).
Figure BDA0001500679050000062
The invention has the following advantages and beneficial effects:
the invention solves the problem that the bit allocation is unreasonable because the complexity of a video sequence image and the state of a buffer area of an encoder are not considered in the code rate control algorithm in the current video coding. The invention constructs the distribution weight based on the comprehensive factor by calculating the sum of the image information entropy and the minimum transform domain absolute error and combining the frame layer fixed weight in the R-lambda model code rate control algorithm, calculates the feedback bit of the buffer area by analyzing the fullness degree of the buffer area, and deduces the calculation formula of the finally distributed bit number of the current frame. The frame layer bit allocation method provided by the invention can improve the bit allocation of the frame layer, improve the rationality of the bit allocation and improve the precision of code rate control.
Drawings
Fig. 1 is a schematic diagram of a coding framework of HEVC, an embodiment of the video coding standard provided by the present invention;
FIG. 2 is a schematic diagram of the relationship between the Lagrangian multiplier λ and the R-D curve;
fig. 3 is a flowchart of a method for allocating bits of a video coding rate control frame layer according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the problems is as follows:
analyzing image textures of a frame layer to obtain an image information entropy EI (entropy information) and a sum of Absolute errors (SATD) (sum of Absolute Transformed difference) value of a minimum transform domain;
step two, according to the obtained EI and SATD and the frame layer fixed weight in the R-lambda model code rate control algorithm
Figure BDA0001500679050000071
Calculating bit distribution weight omega of each frame imagepic；
And step three, calculating the bit number distributed by the frame layer according to the following formula (1).
Figure BDA0001500679050000072
Wherein, TCurrPicIs the number of allocated bits of the current frame; t isGOPIs the number of allocated bits of the current group of pictures (GOP); codedGOPRepresenting the number of coded bits in the GOP; omegapicDistributing weight to the bit of the current frame;
Figure BDA0001500679050000081
weights are assigned to the bits of all the unencoded pictures in the GOP.
And step four, calculating the feedback bit of the buffer area by analyzing the state of the buffer area at the video coding end and setting the fullness degree of the buffer area.
According to the formula (2) and the formula (3), respectivelyAnd calculating to obtain the sum SATD of the image information entropy EI and the minimum transform domain absolute error. Frame layer fixed weight in R-lambda model rate control algorithm according to EI, SATD and current frame image
Figure BDA0001500679050000082
Obtaining distribution weight omega based on comprehensive factors by adopting formula (4)pic。
Figure BDA0001500679050000083
Figure BDA0001500679050000084
Figure BDA0001500679050000085
Where p (x) is the probability of the occurrence of the gray level of image x; n is the maximum gray level; m is the number of pixels of the pixel block row and column; h isi,jThe pixel blocks are corresponding values after Hadamard transformation; and a and b are weighting coefficients, and the value ranges of the weighting coefficients are between 0 and 1.
Calculating feedback bit delta of the buffer according to formulas (5) to (8)T。
Bd＝R/f (5)
L＝μ×Bd (6)
Figure BDA0001500679050000086
ΔT＝η×(L-Tbufleft) (8)
Wherein, BdIs the size of the buffer; r is a channel rate, and the value of the channel rate can be preset in a configuration file; f is a frame rate; deltaTFeedback bits representing a buffer; the values of the coefficients mu and eta are more than 0 and less than 1; l is a target buffer level; t isbufleftRepresenting the amount of remaining data in the buffer; t isbufleftIs the amount of remaining data in the buffer(ii) a i represents the sequence number of the encoded frame; n is the sequence number of the current coded frame;
Figure BDA0001500679050000087
allocating the acquired bit number for the coded image frame in the current group of pictures (GOP);
Figure BDA0001500679050000091
is the number of bits actually consumed in the current group of pictures for the already encoded image frame.
The final frame layer bit number is calculated according to equation (9) below.
Figure BDA0001500679050000092
Wherein, TCurrPicfalRepresenting the finally allocated bit number of the current frame; t isGOPIs the number of allocated bits of the current GOP; codedGOPRepresenting the number of coded bits in the GOP; omegapicDistributing weight to the bit of the current frame;
Figure BDA0001500679050000093
assigning weights to bits of all uncoded pictures in the GOP; r is the channel rate, the value of which can be set in a configuration file in advance, and f is the frame rate; deltaTFeedback bits for the buffer; and gamma is a weighting coefficient, the value range of gamma is between 0 and 1, and the value of gamma is selected according to different configuration files.
Example (b):
a video coding rate control frame layer bit distribution method based on a comprehensive factor and a buffer area state is characterized in that in the process of controlling the rate of video coding, aiming at texture characteristics of different video sequences, a distribution weight based on the comprehensive factor is constructed by calculating the sum of image information entropy and the minimum transform domain absolute error and combining a frame layer fixed weight in an R-lambda model rate control algorithm, and a calculation formula of the finally distributed bit number of a current frame is deduced by considering the fullness degree of the buffer area of the video coding. The invention can improve the rationality of frame layer bit allocation, improve the precision of code rate control and improve the rate distortion performance.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

Claims (5)

1. A method for distributing bits of a video coding rate control frame layer is characterized by comprising the following steps:
step 1, inputting a video, and analyzing frame layer image textures of the video to obtain an image information entropy EI and a SATD value of the sum of absolute errors of a minimum transform domain;
step 2, according to the obtained EI and SATD values and the frame layer fixed weight in the R-lambda model code rate control algorithm
Figure FDA0003215801570000011
Calculating the bit distribution weight omega of each frame imagepic；
Step 3, calculating the bit number distributed by the frame layer according to the formula (1);
Figure FDA0003215801570000012
wherein, TCurrPicIs the number of bits allocated for the current frame; t isGOPIs the bit number allocated by the current group of pictures GOP; codedGOPRepresenting the number of coded bits in the GOP; omegapicDistributing weight to the bit of the current frame;
Figure FDA0003215801570000013
assigning weights to bits of all uncoded pictures in the GOP;
step 4, analyzing the state of the video coding buffer area, and calculating the feedback bit of the buffer area by setting the fullness degree of the buffer area;
step 5, calculating according to step 3Frame layer allocation bit number TCurrPicAnd feedback bit delta of bufferTCalculating the bit number to be allocated to the current frame;
the feedback bit calculation process of the buffer in step 4 is as follows:
calculating a target bit number obtained by distributing the coded image frame, calculating a bit number consumed in the actual coding process of the coded image frame, solving the sum of absolute differences of the two bit numbers, and then calculating the residual bit number of the buffer area through a formula (5);
Figure FDA0003215801570000014
wherein, TbufleftIs the number of remaining bits in the buffer; i represents the sequence number of the encoded frame; n is the serial number of the current frame to be coded;
Figure FDA0003215801570000015
the number of bits allocated to the coded image frame in the current group of pictures GOP;
Figure FDA0003215801570000016
is the number of bits actually consumed by the coded pictures in the current group of pictures; in order to avoid buffer overflow, before encoding an image frame, a target buffer level L is set, so that the fullness of the buffer area after encoding is as close as possible to the value, and the calculation formula of the target buffer level L is shown as (6);
L＝μ×Bd (6)
Bd＝R/f (7)
wherein the coefficient mu is a value between 0 and 1; b isdIs the size of the buffer; r is a channel rate which can be preset in a configuration file; f is a frame rate;
the calculation formula of the feedback bit of the buffer is shown as (8);
ΔT＝η×(L-Tbufleft) (8)
wherein, DeltaTFeedback bits representing a buffer; the coefficient eta is a value between 0 and 1; l is a target buffer level; t isbufleftRepresenting the number of remaining bits of the buffer;
bit number T based on buffer fullness allocationCurrPicbufThe calculation is carried out by adopting a formula (9);
Figure FDA0003215801570000021
2. the method of claim 1, wherein the entropy EI of step 1 is calculated by formula (2);
Figure FDA0003215801570000022
where p (x) is the probability of the occurrence of the gray level of x of the image and N is the maximum gray level of the image.
3. The method of claim 1, wherein the SATD value of the minimum transform domain absolute error sum in step 1 is calculated by formula (3);
Figure FDA0003215801570000023
where M is the number of pixels in the rows and columns of the pixel block, hi,jThe pixel blocks are the corresponding values after Hadamard transformation.
4. The method as claimed in claim 1, wherein the frame layer bits in step 2 are assigned weights ωpicCalculated by formula (4);
Figure FDA0003215801570000031
wherein, EIiIs the information entropy of the current frame image; SATDiThe sum of the minimum transform domain absolute errors of the current frame image is obtained;
Figure FDA0003215801570000032
representing the frame layer fixed weight of the current frame image in an R-lambda model code rate control algorithm; a and b are weighting coefficients, and the two coefficient values are more than 0 and less than 1.
5. The method of claim 1, wherein the step 5 is to obtain the final frame layer bit distribution formula as shown in (10) by using the distribution weight based on the synthesis factor and the buffer feedback bit weighted average;
TCurrPicfal＝γ×TCurrPic+(1-γ)×TCurrPicbuf (10)
wherein, TCurrPicfalRepresenting the finally allocated bit number of the current frame; t isCurrPicThe bit number is distributed to the current frame according to the distribution weight based on the comprehensive factor; t isCurrPicbufIs the number of bits allocated based on buffer fullness; gamma is a weighting coefficient, the value range of gamma is 0-1, and the value is selected according to different configuration files;
simultaneous equations (1), (9) and (10) give T as shown in (11)CurrPicfalThe calculation formula of (2);
Figure FDA0003215801570000033
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