CN102271279B - Objective analysis method for just noticeable change step length of stereo images - Google Patents

Abstract

The invention discloses an objective analysis method for just noticeable change step length of stereo images. The objective analysis method comprises the following steps of: training each distorted stereo image in a distorted stereo image set by using different weight proportion combinations to obtain a support vector regression training model of the different weight proportion combinations; testing any test image by utilizing the support vector regression model obtained by training; in a condition that a left-view image has a constant and invariable quality, measuring a critical quality point of a right-view point image when the quality variation of the stereo image is sensed by human eyes so that a maximum variation range in which the quality of the right-view image can be decreased relative to the quality of the left-view image can be determined when the stereo image is coded. The objective analysis method not only can achieve the purpose of improving coding efficiency by decreasing quality of the right-view image, but also ensures that an observer cannot sense the reduction in the quality of the right-view image through a stereoscopic visual masking effect, thereby ensuring the integral quality of the stereo image.

Description

A kind of objective analysis method of minimum discernable change step of stereo-picture

Technical field

The present invention relates to a kind of measurement and analytical method of stereo-picture vision perception characteristic, especially relate to a kind of objective analysis method of minimum discernable change step of stereo-picture.

Background technology

Compare with the demonstration of two dimension (2D, Two Dimensional) video, three-dimensional/three-dimensional (3D, Three Dimensional) video shows with people's vision and mates more that it makes people be rich in third dimension and feeling of immersion when screen is watched image.In recent years, beautiful, Europe, day, governments such as Korea Spro and enterprise drop into huge fund one after another and carry out stereoscopic TV/three-dimensional television (3DTV, Three Dimensional Television) research and development, comprise the American National Natural Science Fund, research Ministry of Energy of USN, the visual techniques center of United States advanced, the 3DTV plan of European Union's the 6th framework agreement, the ATTEST of European Union plan, the European information technology plan, the educational research Ministry of Science and Technology of moral federal government and Britain's engineering and Physical Science Study Committee etc., Japan and Korea S are developing collection separately and are obtaining, coding, transmission and stereo display are in the 3DTV of one system or have the 3D telecommunication of sense of stereoscopic vision.The ISO/IEC MPEG of International Standards Organization and ITU-T VCEG have also carried out the related work of three-dimensional video-frequency compression applications standard formulation.

Existing psychological study result shows, has masking effect in the stereoscopic vision, namely constitutes two viewpoints of stereo-picture, and the contribution of the total quality of the quality stereoscopic image of the measured visual point image of matter is bigger.Therefore, can utilize this characteristic of human visual system,, suitably reduces a certain visual point image the method for another view-point image quality by being kept its high-quality, guaranteeing under the impregnable situation of the whole subjective quality of stereo-picture, fully remove the redundancy in the vision signal, improve code efficiency.The perception experimental result of plane picture shows, human eye is non to changing less attribute or noise in the image, unless the change intensity of this attribute or noise surpasses a certain threshold value, this threshold value is exactly minimum discernable change step (Just Noticeable Difference, JND), there is the threshold value of masking effect equally in stereoscopic vision.

But the minimum discernable change step when mainly measuring the variation of human eye perception stereoscopic vision by subjective experiment at present, but subjective experiment is subjected to the influence of various factorss such as objective condition, subjective mood and observer's self-condition easily, evaluation result is stable inadequately, and subjective experiment is very consuming time, cost is very high.Because human eye is different to the stereoscopic vision masking effect of different stereo-pictures, its corresponding minimum discernable change step may be also different, and therefore, it is very necessary how analyzing this minimum discernable change step by objective method.

Summary of the invention

Technical problem to be solved by this invention provides a kind of maximum changing range that the quality of the left relatively visual point image of quality of right visual point image can descend can determine asymmetric stereo scopic video coding well the time, reach the purpose that improves the coding compression efficiency by the quality that reduces right visual point image, utilize the stereoscopic vision masking effect to make the observer can not perceive the decline of right view-point image quality simultaneously, can effectively guarantee the objective analysis method of the minimum discernable change step of human eye of the total quality of stereo-picture.

The present invention solves the problems of the technologies described above the technical scheme that adopts: a kind of objective analysis method of minimum discernable change step of stereo-picture is characterized in that may further comprise the steps:

1. make S _OrgUndistorted stereo-picture for original makes S _DisFor the stereo-picture of distortion to be evaluated, with S _OrgLeft visual point image be designated as L _Org, with S _OrgRight visual point image be designated as R _Org, with S _DisLeft visual point image be designated as L _Dis, with S _DisRight visual point image be designated as R _Dis

2. to L _Org, R _Org, L _DisAnd R _Dis4 width of cloth images are implemented singular value decomposition respectively, obtain L respectively _Org, R _Org, L _DisAnd R _DisEach self-corresponding singular value vector of 4 width of cloth images is with L _OrgThe singular value vector be designated as

With R _OrgThe singular value vector be designated as

With L _DisThe singular value vector be designated as

With R _DisThe singular value vector be designated as Wherein, the dimension of each singular value vector is m, and m=min (M, N), min () is for getting minimum value function, the horizontal size size of M presentation video, the vertical dimension size of N presentation video;

3. calculate L _OrgThe singular value vector

With L _DisThe singular value vector

The absolute difference vector, be designated as X ^L,

With X ^LAs L _DisCharacteristic vector, calculate R _OrgThe singular value vector

With R _DisThe singular value vector

The absolute difference vector, be designated as X ^R,

With X ^RAs R _DisCharacteristic vector, wherein, " || " is the symbol that takes absolute value;

4. to L _DisCharacteristic vector X ^LAnd R _DisCharacteristic vector X ^RCarry out linear weighted function, obtain S _DisCharacteristic vector, be designated as X, X=w _LX ^L+ w _RX ^R, wherein, w _LExpression L _DisWeights proportion, w _RExpression R _DisWeights proportion, w _L+ w _R=1;

5. adopt n undistorted stereo-picture, set up its distortion stereo-picture set under the different distortion levels of coding distortion type H.264, this distortion stereo-picture set comprises the stereo-picture of several distortions, utilizes the subjective quality evaluation method to obtain the average subjective scoring difference of the stereo-picture of every width of cloth distortion in the set of distortion stereo-picture respectively, is designated as DMOS, DMOS=100-MOS, wherein, MOS represents the subjective scoring average, DMOS ∈ [0,100], n 〉=1;

6. adopt and calculating S _DisThe identical method of characteristic vector X, the different weights proportion combined feature vectors of the stereo-picture of every width of cloth distortion during the calculated distortion stereo-picture is gathered respectively, j kind weights proportion combined feature vector for the stereo-picture of i width of cloth distortion in the set of distortion stereo-picture is designated as X with it _{I, j}, wherein, 1≤i≤n', 1≤j≤m', n' represent the width of cloth number of the stereo-picture of the distortion that comprises in the distortion stereo-picture set, m' represents the kind number of all weights proportions combinations;

7. adopt support vector regression that the identical weights proportion combined feature vector of the stereo-picture of distortions all in the set of distortion stereo-picture is trained, obtain different weights than the support vector regression training pattern of recombination, for the support vector regression training pattern of j kind weights than recombination, it is designated as f _j(X _Inp), wherein, f _j() is that j kind weights are than the function representation form of the regression function of recombination, X _InpExpress support for the input vector of vector regression training pattern;

8. from n undistorted stereo-picture, choose a width of cloth stereo-picture arbitrarily, appoint then and get a coded quantization parameter the left visual point image of this stereo-picture is encoded, and adopt a plurality of different coded quantization parameters that the right visual point image of this stereo-picture is encoded, utilize support vector regression training pattern that training obtains that several test patterns that the width of cloth left side visual point image that obtained by coding and several right visual point images constitute are tested, but calculate the minimum discernable change step of human eye perception stereoscopic vision right visual point image in the test pattern when changing.

Described step detailed process 2. is:

2.-1, with size be the L of M * N _OrgBe expressed as the two-dimensional matrix of M * N dimension, be designated as

By the two-dimensional matrix of singular value decomposition with M * N dimension

Be expressed as Wherein,

The orthogonal matrix of expression M * M dimension, The orthogonal matrix of expression N * N dimension, Expression

Transposed matrix,

The diagonal matrix of expression M * N dimension;

2.-2, the diagonal matrix that M * N is tieed up

Diagonal element as the two-dimensional matrix of M * N dimension

Singular value, from the two-dimensional matrix of M * N dimension

Singular value in take out the singular value formation L of m non-zero _OrgThe singular value vector, be designated as

Wherein, and m=min (M, N), min () is for getting minimum value function;

2.-3, to R _Org, L _DisAnd R _DisAdopt with step 2.-1 to 2.-2 identical operations, obtain R _Org, L _DisAnd R _DisThe singular value vector, be designated as respectively

With

Described step detailed process 7. is:

7.-1, with the identical weights proportion combined feature vector of the stereo-picture of distortions all in the distortion stereo-picture set and average subjective scoring difference as the set of training sample data, j kind weights are designated as Ω than the training sample data set of recombination _{Q, j}, { X _{K, j}, DMOS _k∈ Ω _{Q, j}, wherein, 1≤j≤m', m' represent the kind number of all weights proportion combinations, q represents that j kind weights are than the training sample data set omega of recombination _{Q, j}In the width of cloth number of stereo-picture of the distortion that comprises, X _{K, j}Represent that j kind weights are than the training sample data set omega of recombination _{Q, j}In the characteristic vector of stereo-picture of k width of cloth distortion, DMOS _kRepresent that j kind weights are than the training sample data set omega of recombination _{Q, j}In the average subjective scoring difference of stereo-picture of k width of cloth distortion, 1≤k≤q;

7.-2, structure X _{K, j}Regression function f _j(X _{K, j}),

Wherein, f _j() be j kind weights than the function representation form of the regression function of recombination, w is weight vector, w ^TBe the transposed matrix of w, b is bias term, Represent that j kind weights are than the training sample data set omega of recombination _{Q, j}In the characteristic vector X of stereo-picture of k width of cloth distortion _{K, j}Linear function,

Be the kernel function in the support vector regression,

X _{L, j}Be that j kind weights are than the training sample data set omega of recombination _{Q, j}In the characteristic vector of stereo-picture of l width of cloth distortion, γ is nuclear parameter, is used for the scope of reflection input sample value, the scope of sample value is more big, and the γ value is also just more big, and exp () expression is the exponential function at the end with e, e=2.71828183, " || || " for asking the Euclidean distance symbol;

7.-3, adopt support vector regression to the training sample data set omega of j kind weights than recombination _{Q, j}In the characteristic vector of stereo-picture of all distortion train, make error minimum between the regression function value that obtains through training and the average subjective scoring difference, match obtains the weight vector w of optimum ^OptBias term b with optimum ^Opt, with the weight vector w of optimum ^OptBias term b with optimum ^OptCombination be designated as (w ^Opt, b ^Opt), $(w^{\mathrm{opt}},b^{\mathrm{opt}})=\underset{(w,b)\∈\mathrm{\Ψ}}{\arg \min }\underset{k=1}{\overset{q}{\mathrm{\Σ}}}{(f_j\left(X_{k,j}\right)-{\mathrm{DMOS}}_k)}^2,$ Utilize optimum weight vector w ^OptBias term b with optimum ^OptConstruct j kind weights than the support vector regression training pattern of recombination, be designated as f _j(X _Inp),

Wherein, Ψ represents the training sample data set omega of j kind weights than recombination _{Q, j}In the set of combination of the characteristic vector of stereo-picture of all distortion all weight vector of training and bias term, Expression minimizes probability density function, X _InpExpress support for the input vector of vector regression training pattern, (w ^Opt) ^TBe w ^OptTransposed matrix,

Express support for the input vector X of vector regression training pattern _InpLinear function.

Described step detailed process 8. is:

8.-1, from n undistorted stereo-picture, choose a width of cloth stereo-picture arbitrarily, appoint and get a coded quantization parameter as the basic coding quantization parameter of the left visual point image of this stereo-picture, be designated as QP _L, will adopt QP _LThe encode left visual point image that obtains of the left visual point image of this stereo-picture is defined as the left visual point image of A quality point, is designated as L _A

8.-2, obtain five values more than or equal to QP _LAnd the coded quantization parameter that value has nothing in common with each other is as the basic coding quantization parameter of the right visual point image of this stereo-picture, adopt these five basic coding quantization parameters respectively the right visual point image of this stereo-picture to be encoded, obtain the right visual point image that five width of cloth quality have nothing in common with each other, be defined as the right visual point image of A quality point, the right visual point image of B quality point, the right visual point image of C quality point, the right visual point image of D quality point, the right visual point image of E quality point respectively, be designated as R respectively _A, R _B, R _C, R _D, R _E, wherein, with the right visual point image R of A quality point _AThe basic coding quantization parameter that adopts during coding is designated as QP _RA, with the right visual point image R of B quality point _BThe basic coding quantization parameter that adopts during coding is designated as QP _RB, with the right visual point image R of C quality point _CThe basic coding quantization parameter that adopts during coding is designated as QP _RC, with the right visual point image R of D quality point _DThe basic coding quantization parameter that adopts during coding is designated as QP _RD, with the right visual point image R of E quality point _EThe basic coding quantization parameter that adopts during coding is designated as QP _RE

8.-3, with the left visual point image L of A quality point _ARight visual point image R with the A quality point _AThe stereo-picture that constitutes is as the 1st width of cloth test pattern, and is designated as S _AALeft visual point image L with the A quality point _ARight visual point image R with the B quality point _BThe stereo-picture that constitutes is as the 2nd width of cloth test pattern, and is designated as S _ABLeft visual point image L with the A quality point _ARight visual point image R with the C quality point _CThe stereo-picture that constitutes is as the 3rd width of cloth test pattern, and is designated as S _ACLeft visual point image L with the A quality point _ARight visual point image R with the D quality point _DThe stereo-picture that constitutes is as the 4th width of cloth test pattern, and is designated as S _ADLeft visual point image L with the A quality point _ARight visual point image R with the E quality point _EThe stereo-picture that constitutes is as the 5th width of cloth test pattern, and is designated as S _AE

8.-4, adopt and calculate S _DisThe identical method of characteristic vector X, calculate S _AAAdopt different weights proportion combined feature vectors, with S _AAAdopt j kind weights proportion combined feature vector to be designated as X _{A, j}, wherein, 1≤j≤m', m' represent the kind number of all weights proportion combinations;

8.-5, according to the support vector regression training pattern, the prediction S _AAAdopt the evaluating objective quality predicted value of all different weights proportion combined feature vectors, for S _AAAdopt j kind weights proportion combined feature vector X _{A, j}, with X _{A, j}As the input vector of j kind weights than the support vector regression training pattern of recombination, prediction obtains X _{A, j}The evaluating objective quality predicted value, be designated as Q _j, Q _j=f _j(X _{A, j}),

Wherein, w ^OptBe the weight vector of optimum, (w ^Opt) ^TBe w ^OptTransposed matrix, b ^OptBe the bias term of optimum,

Expression X _{A, j}Linear function;

8.-6, with S _AAAdopt the mean value of evaluating objective quality predicted value of all different weights proportion combined feature vectors as S _AAThe objective evaluation value, be designated as Score _A,

8.-7,8.-4 employing and step to 8.-6 identical operations, obtain S _AB, S _AC, S _ADAnd S _AEThe objective evaluation value, be designated as Score respectively _B, Score _C, Score _DAnd Score _E

8.-8, calculate S _AA, S _AB, S _AC, S _ADAnd S _AEObjective score value, be designated as D respectively ₁, D ₂, D ₃, D ₄And D ₅, D ₁=0, $D_2=\frac{{\mathrm{Score}}_B-{\mathrm{Score}}_A}{{\mathrm{QP}}_{\mathrm{RB}}-{\mathrm{QP}}_{\mathrm{RA}}},$ $D_3=\frac{{\mathrm{Score}}_C-{\mathrm{Score}}_B}{{\mathrm{QP}}_{\mathrm{RC}}-{\mathrm{QP}}_{\mathrm{RB}}},$ ${\mathrm{<figure-callout\; id="4"\; label="D"\; filenames="HDA0000077667530000041.png,HDA0000077667530000042.png"\; state="\{\{state\}\}">D</figure-callout>}}_4=\frac{{\mathrm{Score}}_D-{\mathrm{Score}}_C}{{\mathrm{QP}}_{\mathrm{RD}}-{\mathrm{QP}}_{\mathrm{RC}}},$ $D_5=\frac{{\mathrm{Score}}_E-{\mathrm{Score}}_D}{{\mathrm{QP}}_{\mathrm{RE}}-{\mathrm{QP}}_{\mathrm{RD}}};$

8.-9, from { D _J'| the maximum objective score value of the value of finding out among 1≤j'≤5} is designated as D _Max, with D _MaxThe quality point of corresponding right visual point image is designated as q, the stereo-picture that is constituted by the left visual point image of the right visual point image of q quality point and A quality point then, and the picture quality difference that comes with the English alphabet order between the stereo-picture of the corresponding right visual point image of each quality point and the left visual point image formation of A quality point before the q quality point is distinguishable, the difference of the Y-PSNR of the Y-PSNR of the right visual point image of definition q quality point and the right visual point image of A quality point is as adopting QP _LHuman eye was to the discernable change step of appreciable minimum of asymmetric stereo image coding when left visual point image was encoded.

Described step 6. in the process of the different weights proportion combined feature vectors of the stereo-picture of calculated distortion, j kind weights are designated as (w than recombination _L', w _R'), w _R'=w ₀+ (j-1) * and Δ w, w _L'=1-w _R', wherein, w _L' the weights proportion of left visual point image of stereo-picture of expression distortion, w _R' the weights proportion of right visual point image of stereo-picture of expression distortion, w ₀=0.55, Δ w=0.05,1≤j≤m', m' represent the kind number of all weights proportion combinations.

Compared with prior art, the invention has the advantages that:

1) the inventive method is mapped in the high-dimensional feature space by the characteristic vector of support vector regression with stereo-picture, in high-dimensional feature space, carry out Linear Estimation again, the characteristic vector of structure optimum regression function stereoscopic image is tested, avoided human visual system's correlation properties and the complicated simulation process of mechanism, and because training sample and test sample book are separate, can avoid test result to the depending on unduly of training data like this, the maximum changing range that the quality of the left relatively visual point image of quality of right visual point image can descend in the time of determining asymmetric stereo scopic video coding better.

2) the inventive method adopts different weights proportion combinations to train by the stereo-picture to the every width of cloth distortion in the set of distortion stereo-picture, obtain different weights than the support vector regression training pattern of recombination, the support vector regression model that utilizes training to obtain is tested any test pattern, under the changeless situation of left view-point image quality, but the critical mass point of right view-point image quality when measuring the variation of human eye perception stereo image quality, thereby determine the maximum changing range that the left relatively view-point image quality of right view-point image quality can descend when stereoscopic image was encoded, can reach the purpose that improves code efficiency by reducing right view-point image quality, can utilize the stereoscopic vision masking effect to make the observer can not perceive the fact that right view-point image quality descends again, thereby guarantee the total quality of stereo-picture.

Description of drawings

Fig. 1 is the overall realization block diagram of the inventive method;

Fig. 2 a is that Akko(is of a size of 640 * 480) the left visual point image of stereo-picture;

Fig. 2 b is that Akko(is of a size of 640 * 480) the right visual point image of stereo-picture;

Fig. 3 a is that Altmoabit(is of a size of 1024 * 768) the left visual point image of stereo-picture;

Fig. 3 b is that Altmoabit(is of a size of 1024 * 768) the right visual point image of stereo-picture;

Fig. 4 a is that Balloons(is of a size of 1024 * 768) the left visual point image of stereo-picture;

Fig. 4 b is that Balloons(is of a size of 1024 * 768) the right visual point image of stereo-picture;

Fig. 5 a is that Doorflower(is of a size of 1024 * 768) the left visual point image of stereo-picture;

Fig. 5 b is that Doorflower(is of a size of 1024 * 768) the right visual point image of stereo-picture;

Fig. 6 a is that Kendo(is of a size of 1024 * 768) the left visual point image of stereo-picture;

Fig. 6 b is that Kendo(is of a size of 1024 * 768) the right visual point image of stereo-picture;

Fig. 7 a is that LeaveLaptop(is of a size of 1024 * 768) the left visual point image of stereo-picture;

Fig. 7 b is that LeaveLaptop(is of a size of 1024 * 768) the right visual point image of stereo-picture;

Fig. 8 a is that Lovebierd1(is of a size of 1024 * 768) the left visual point image of stereo-picture;

Fig. 8 b is that Lovebierd1(is of a size of 1024 * 768) the right visual point image of stereo-picture;

Fig. 9 a is that Newspaper(is of a size of 1024 * 768) the left visual point image of stereo-picture;

Fig. 9 b is that Newspaper(is of a size of 1024 * 768) the right visual point image of stereo-picture;

Figure 10 a is that Puppy(is of a size of 720 * 480) the left visual point image of stereo-picture;

Figure 10 b is that Puppy(is of a size of 720 * 480) the right visual point image of stereo-picture;

Figure 11 a is that Soccer2(is of a size of 720 * 480) the left visual point image of stereo-picture;

Figure 11 b is that Soccer2(is of a size of 720 * 480) the right visual point image of stereo-picture;

Figure 12 a is that Horse(is of a size of 720 * 480) the left visual point image of stereo-picture;

Figure 12 b is that Horse(is of a size of 720 * 480) the right visual point image of stereo-picture;

Figure 13 a is that Xmas(is of a size of 640 * 480) the left visual point image of stereo-picture;

Figure 13 b is that Xmas(is of a size of 640 * 480) the right visual point image of stereo-picture;

Figure 14 is the schematic diagram that concerns of the quality of the left visual point image of " Altmoabit " and " Doorflowers " test pattern of obtaining through the inventive method and minimum discernable change step.

Embodiment

Describe in further detail below in conjunction with the present invention of accompanying drawing embodiment.

The objective analysis method of the minimum discernable change step of a kind of stereo-picture that the present invention proposes, it totally realizes block diagram as shown in Figure 1, it mainly may further comprise the steps:

1. make S _OrgUndistorted stereo-picture for original makes S _DisFor the stereo-picture of distortion to be evaluated, with S _OrgLeft visual point image be designated as L _Org, with S _OrgRight visual point image be designated as R _Org, with S _DisLeft visual point image be designated as L _Dis, with S _DisRight visual point image be designated as R _Dis

2. to L _Org, R _Org, L _DisAnd R _Dis4 width of cloth images are implemented singular value decomposition respectively, obtain L respectively _Org, R _Org, L _DisAnd R _DisEach self-corresponding singular value vector of 4 width of cloth images is with L _OrgThe singular value vector be designated as

With R _OrgThe singular value vector be designated as

With L _DisThe singular value vector be designated as

With R _DisThe singular value vector be designated as Wherein, the dimension of each singular value vector is m, and (M, N), min () is for getting minimum value function, the horizontal size size of M presentation video, the vertical dimension size of N presentation video for m=min.

In the present embodiment, step detailed process 2. is:

2.-1, with size be the L of M * N _OrgBe expressed as the two-dimensional matrix of M * N dimension, be designated as

By the two-dimensional matrix of singular value decomposition with M * N dimension

Be expressed as

Wherein, The orthogonal matrix of expression M * M dimension,

The orthogonal matrix of expression N * N dimension,

Expression

Transposed matrix,

The diagonal matrix of expression M * N dimension.

2.-2, the diagonal matrix that M * N is tieed up

Diagonal element as the two-dimensional matrix of M * N dimension Singular value, from the two-dimensional matrix of M * N dimension

Singular value in take out the singular value formation L of m non-zero _OrgThe singular value vector, be designated as

Wherein, (M, N), min () is for getting minimum value function for m=min.

2.-3, to R _Org, L _DisAnd R _DisAdopt with step 2.-1 to 2.-2 identical operations, obtain R _Org, L _DisAnd R _DisThe singular value vector, be designated as respectively

With

4. to L _DisCharacteristic vector X ^LAnd R _DisCharacteristic vector X ^RCarry out linear weighted function, obtain S _DisCharacteristic vector, be designated as X, X=w _LX ^L+ w _RX ^R, wherein, w _LExpression L _DisWeights proportion, w _RExpression R _DisWeights proportion, w _L+ w _R=1.

5. adopt n undistorted stereo-picture, set up its distortion stereo-picture set under the different distortion levels of coding distortion type H.264, this distortion stereo-picture set comprises the stereo-picture of several distortions, utilizes the subjective quality evaluation method to obtain the average subjective scoring difference of the stereo-picture of every width of cloth distortion in the set of distortion stereo-picture respectively, is designated as DMOS, DMOS=100-MOS, wherein, MOS represents the subjective scoring average, DMOS ∈ [0,100], n 〉=1.

In the present embodiment, because the stereo-picture of test obtains by H.264 encoding, therefore the type of distortion of training sample and test sample book should be consistent in support vector regression, utilize the stereo-picture as Fig. 2 a and Fig. 2 b formation, the stereo-picture that Fig. 3 a and Fig. 3 b constitute, the stereo-picture that Fig. 4 a and Fig. 4 b constitute, the stereo-picture that Fig. 5 a and Fig. 5 b constitute, the stereo-picture that Fig. 6 a and Fig. 6 b constitute, the stereo-picture that Fig. 7 a and Fig. 7 b constitute, the stereo-picture that Fig. 8 a and Fig. 8 b constitute, the stereo-picture that Fig. 9 a and Fig. 9 b constitute, the stereo-picture that Figure 10 a and Figure 10 b constitute, the stereo-picture that Figure 11 a and Figure 11 b constitute, the stereo-picture that Figure 12 a and Figure 12 b constitute, the stereo-picture that Figure 13 a and Figure 13 b the constitute undistorted stereo-picture of totally 12 width of cloth (n=12) has been set up its distortion stereo-picture set under the different distortion levels of coding distortion type H.264, and the stereo-picture of distortion has 72 width of cloth in this distortion stereo-picture set.

6. adopt and calculating S _DisThe identical method of characteristic vector X, the different weights proportion combined feature vectors of the stereo-picture of every width of cloth distortion during the calculated distortion stereo-picture is gathered respectively, j kind weights proportion combined feature vector for the stereo-picture of i width of cloth distortion in the set of distortion stereo-picture is designated as X with it _{I, j}, wherein, 1≤i≤n', 1≤j≤m', n' represent the width of cloth number of the stereo-picture of the distortion that comprises in the distortion stereo-picture set, m' represents the kind number of all weights proportions combinations.

In this specific embodiment, because the quality of right visual point image changes in the test pattern, and the stereoscopic vision masking effect of the stereo-picture that human eye constitutes the left visual point image by the right visual point image of different quality and equal in quality is inconsistent, in order can be better the stereoscopic vision masking effect of human eye to be described, the inventive method adopts different weights proportion that the left visual point image of the stereo-picture of every width of cloth distortion and the characteristic vector of right visual point image in the set of distortion stereo-picture are carried out linear weighted function.The inventive method is designated as (w with j kind weights than recombination in the process of the different weights proportion combined feature vectors of the stereo-picture of calculated distortion _L', w _R'), w _R'=w ₀+ (j-1) * and Δ w, w _L'=1-w _R', wherein, w _L' the weights proportion of left visual point image of stereo-picture of expression distortion, w _R' the weights proportion of right visual point image of stereo-picture of expression distortion, w ₀=0.55, Δ w=0.05.

At this, get m'=9.

7. because the characteristic vector of the stereo-picture of distortion is the higher dimensional space vector, need in higher dimensional space, construct linear decision function and realize non-linear decision function in the former space, (Support Vector Regression SVR) is the method for the non-linear higher dimensional space conversion of a kind of reasonable realization to support vector regression.Therefore the inventive method adopts support vector regression that the identical weights proportion combined feature vector of the stereo-picture of distortions all in the set of distortion stereo-picture is trained, obtain different weights than the support vector regression training pattern of recombination, for the support vector regression training pattern of j kind weights than recombination, it is designated as f _j(X _Inp), wherein, f _j() is that j kind weights are than the function representation form of the regression function of recombination, X _InpExpress support for the input vector of vector regression training pattern.

In the present embodiment, step detailed process 7. is:

7.-1, with the identical weights proportion combined feature vector of the stereo-picture of distortions all in the distortion stereo-picture set and average subjective scoring difference as the set of training sample data, j kind weights are designated as Ω than the training sample data set of recombination _{Q, j}, { X _{K, j}, DMOS _k∈ Ω _{Q, j}, wherein, 1≤j≤m', m' represent the kind number of all weights proportion combinations, q represents that j kind weights are than the training sample data set omega of recombination _{Q, j}In the width of cloth number of stereo-picture of the distortion that comprises, X _{K, j}Represent that j kind weights are than the training sample data set omega of recombination _{Q, j}In the characteristic vector of stereo-picture of k width of cloth distortion, DMOS _kRepresent that j kind weights are than the training sample data set omega of recombination _{Q, j}In the average subjective scoring difference of stereo-picture of k width of cloth distortion, 1≤k≤q.

7.-2, structure X _{K, j}Regression function f _j(X _{K, j}), Wherein, f _j() be j kind weights than the function representation form of the regression function of recombination, w is weight vector, w ^TBe the transposed matrix of w, b is bias term,

Represent that j kind weights are than the training sample data set omega of recombination _{Q, j}In the characteristic vector X of stereo-picture of k width of cloth distortion _{K, j}Linear function,

Be the kernel function in the support vector regression, X _{L, j}Be that j kind weights are than the training sample data set omega of recombination _{Q, j}In the characteristic vector of stereo-picture of l width of cloth distortion, γ is nuclear parameter, is used for the scope of reflection input sample value, the scope of sample value is more big, and the γ value is also just more big, and exp () expression is the exponential function at the end with e, e=2.71828183, " || || " for asking the Euclidean distance symbol.

7.-3, adopt support vector regression to the training sample data set omega of j kind weights than recombination _{Q, j}In the characteristic vector of stereo-picture of all distortion train, make error minimum between the regression function value that obtains through training and the average subjective scoring difference, match obtains the weight vector w of optimum ^OptBias term b with optimum ^Opt, with the weight vector w of optimum ^OptBias term b with optimum ^OptCombination be designated as (w ^Opt, b ^Opt), $(w^{\mathrm{opt}},b^{\mathrm{opt}})=\underset{(w,b)\&Element;\mathrm{\&Psi;}}{\arg \min }\underset{k=1}{\overset{q}{\mathrm{\&Sigma;}}}{(f_j\left(X_{k,j}\right)-{\mathrm{DMOS}}_k)}^2,$ Utilize optimum weight vector w ^OptBias term b with optimum ^OptConstruct j kind weights than the support vector regression training pattern of recombination, be designated as f _j(X _Inp),

Wherein, Ψ represents the training sample data set omega of j kind weights than recombination _{Q, j}In the set of combination of the characteristic vector of stereo-picture of all distortion all weight vector of training and bias term,

Expression minimizes probability density function, X _InpExpress support for the input vector of vector regression training pattern, (w ^Opt) ^TBe w ^OptTransposed matrix,

Express support for the input vector X of vector regression training pattern _InpLinear function.

8. gather the support vector regression training pattern that obtains by training by the distortion stereo-picture, can reflect that the mass change of left visual point image and right visual point image is to the influence of the stereoscopic vision masking effect of human eye, therefore, can utilize this model that the quality of any stereo-picture is measured.From n undistorted stereo-picture, choose a width of cloth stereo-picture arbitrarily, appoint then and get a coded quantization parameter the left visual point image of this stereo-picture is encoded, and adopt a plurality of different coded quantization parameters that the right visual point image of this stereo-picture is encoded, utilize support vector regression training pattern that training obtains that several test patterns that the width of cloth left side visual point image that obtained by coding and several right visual point images constitute are tested, but calculate the minimum discernable change step of human eye perception stereoscopic vision right visual point image in the test pattern when changing.

In the present embodiment, step detailed process 8. is:

8.-1, from n undistorted stereo-picture, choose a width of cloth stereo-picture arbitrarily, appoint and get a coded quantization parameter as the basic coding quantization parameter of the left visual point image of this stereo-picture, be designated as QP _L, will adopt QP _LThe encode left visual point image that obtains of the left visual point image of this stereo-picture is defined as the left visual point image of A quality point, is designated as L _A

8.-2, obtain five values more than or equal to QP _LAnd the coded quantization parameter that value has nothing in common with each other is as the basic coding quantization parameter of the right visual point image of this stereo-picture, adopt these five basic coding quantization parameters respectively the right visual point image of this stereo-picture to be encoded, obtain the right visual point image that five width of cloth quality have nothing in common with each other, be defined as the right visual point image of A quality point, the right visual point image of B quality point, the right visual point image of C quality point, the right visual point image of D quality point, the right visual point image of E quality point respectively, be designated as R respectively _A, R _B, R _C, R _D, R _E, wherein, with the right visual point image R of A quality point _AThe basic coding quantization parameter that adopts during coding is designated as QP _RA, with the right visual point image R of B quality point _BThe basic coding quantization parameter that adopts during coding is designated as QP _RB, with the right visual point image R of C quality point _CThe basic coding quantization parameter that adopts during coding is designated as QP _RC, with the right visual point image R of D quality point _DThe basic coding quantization parameter that adopts during coding is designated as QP _RD, with the right visual point image R of E quality point _EThe basic coding quantization parameter that adopts during coding is designated as QP _RE

8.-3, with the left visual point image L of A quality point _ARight visual point image R with the A quality point _AThe stereo-picture that constitutes is as the 1st width of cloth test pattern, and is designated as S _AALeft visual point image L with the A quality point _ARight visual point image R with the B quality point _BThe stereo-picture that constitutes is as the 2nd width of cloth test pattern, and is designated as S _ABLeft visual point image L with the A quality point _ARight visual point image R with the C quality point _CThe stereo-picture that constitutes is as the 3rd width of cloth test pattern, and is designated as S _ACLeft visual point image L with the A quality point _ARight visual point image R with the D quality point _DThe stereo-picture that constitutes is as the 4th width of cloth test pattern, and is designated as S _ADLeft visual point image L with the A quality point _ARight visual point image R with the E quality point _EThe stereo-picture that constitutes is as the 5th width of cloth test pattern, and is designated as S _AE

8.-4, adopt and calculate S _DisThe identical method of characteristic vector X, calculate S _AAAdopt different weights proportion combined feature vectors, with S _AAAdopt j kind weights proportion combined feature vector to be designated as X _{A, j}, wherein, 1≤j≤m', m' represent the kind number of all weights proportion combinations.

8.-5, according to the support vector regression training pattern, the prediction S _AAAdopt the evaluating objective quality predicted value of all different weights proportion combined feature vectors, for S _AAAdopt j kind weights proportion combined feature vector X _{A, j}, with X _{A, j}As the input vector of j kind weights than the support vector regression training pattern of recombination, prediction obtains X _{A, j}The evaluating objective quality predicted value, be designated as Q _j, Q _j=f _j(X _{A, j}), Wherein, w ^OptBe the weight vector of optimum, (w ^Opt) ^TBe w ^OptTransposed matrix, b ^OptBe the bias term of optimum,

Expression X _{A, j}Linear function.

8.-6, with S _AAAdopt the mean value of evaluating objective quality predicted value of all different weights proportion combined feature vectors as S _AAThe objective evaluation value, be designated as Score _A,

8.-7,8.-4 employing and step to 8.-6 identical operations, obtain S _AB, S _AC, S _ADAnd S _AEThe objective evaluation value, be designated as Score respectively _B, Score _C, Score _DAnd Score _E

8.-8, calculate S _AA, S _AB, S _AC, S _ADAnd S _AEObjective score value, be designated as D respectively ₁, D ₂, D ₃, D ₄And D ₅, D ₁=0, $D_2=\frac{{\mathrm{Score}}_B-{\mathrm{Score}}_A}{{\mathrm{QP}}_{\mathrm{RB}}-{\mathrm{QP}}_{\mathrm{RA}}},$ $D_3=\frac{{\mathrm{Score}}_C-{\mathrm{Score}}_B}{{\mathrm{QP}}_{\mathrm{RC}}-{\mathrm{QP}}_{\mathrm{RB}}},$ ${\mathrm{<figure-callout\; id="4"\; label="D"\; filenames="HDA0000077667530000041.png,HDA0000077667530000042.png"\; state="\{\{state\}\}">D</figure-callout>}}_4=\frac{{\mathrm{Score}}_D-{\mathrm{Score}}_C}{{\mathrm{QP}}_{\mathrm{RD}}-{\mathrm{QP}}_{\mathrm{RC}}},$ $D_5=\frac{{\mathrm{Score}}_E-{\mathrm{Score}}_D}{{\mathrm{QP}}_{\mathrm{RE}}-{\mathrm{QP}}_{\mathrm{RD}}}.$

8.-9, from { D _J'| 1≤j'≤5} is D ₁, D ₂, D ₃, D ₄And D ₅In the maximum objective score value of the value of finding out, be designated as D _Max, with D _MaxThe quality point of corresponding right visual point image is designated as q, the stereo-picture that is constituted by the left visual point image of the right visual point image of q quality point and A quality point then, and the picture quality difference that comes with the English alphabet order between the stereo-picture of the corresponding right visual point image of each quality point and the left visual point image formation of A quality point before the q quality point is distinguishable, the difference of the Y-PSNR of the Y-PSNR of the right visual point image of definition q quality point and the right visual point image of A quality point is as adopting QP _LHuman eye was to the discernable change step of appreciable minimum of asymmetric stereo image coding when left visual point image was encoded.

Below just utilize the inventive method that the performance that " Altmoabit " and " Doorflowers " stereo-picture carries out evaluating objective quality is compared.

Table 1 and table 2 have provided left visual point image (the i.e. left visual point image L of the A quality point of four width of cloth different qualities of four different qualities of " Altmoabit " and " Doorflowers " test pattern respectively _A) and their Y-PSNR (PSNR) and corresponding codes quantization parameter QP of the right visual point image of corresponding A, B, C, D, five quality point of E separately _RA, QP _RB, QP _RC, QP _RD, QP _RETable 3 and table 4 have provided the objective evaluation value of the stereo-picture that the right visual point image by the left visual point image of A quality point and five different quality points of " Altmoabit " and " Doorflowers " test pattern constitutes respectively.The quality of the more high explanation stereo-picture of objective evaluation value is more poor.From table 3 and table 4 as can be seen, decline along with the quality of left viewpoint or right visual point image, the objective evaluation value of stereo-picture can be more good more high, but because the objective evaluation value of different quality point is the variation tendency that low-key increases progressively, be difficult to from this variation tendency, find the critical quality point of right visual point image, therefore need further to calculate the objective score value of the right visual point image of each quality point.

Table 5 and table 6 have provided the objective score value of the stereo-picture that the right visual point image by the left visual point image of A quality point and five different quality points of " Altmoabit " and " Doorflowers " test pattern constitutes, maximizing from all objective score values easily respectively.For example, " Altmoabit " test pattern is at QP _L=23 o'clock, because the objective score value of the stereo-picture that the objective score value of the stereo-picture that the right visual point image of E quality point constitutes obviously constitutes greater than the right visual point image of other quality point, when the quality of left visual point image remains unchanged, and the quality of right visual point image is when dropping to the E quality point from the A quality point, the variation of the stereo-picture subjective perception that the mass change of the discernable right visual point image of human eye causes, because the right visual point image PSNR=41.794 of A quality point, the right visual point image PSNR=36.763 of E quality point, therefore, Ci Shi human eye is approximately 5.031dB to the discernable change step of appreciable minimum of asymmetric stereo scopic video coding.

Figure 14 has provided the schematic diagram that concerns of the quality of left visual point image of " Altmoabit " and " Doorflowers " test pattern that obtains through the inventive method and minimum discernable change step, decline (the PSNR value reduces) along with the quality of the left visual point image of stereo-picture, minimum discernable change step also can descend, and namely the difference of the PSNR of left visual point image and right visual point image diminishes.The quality of the left visual point image that obtains by subjective experiment in the patent 201010184200.9 of the quality of the left visual point image that obtains by the inventive method and the relation of minimum discernable change step and disclosed application on the 22nd September in 2010 and the relation of minimum discernable change step are very identical, illustrate that the inventive method is effective and feasible.

Y-PSNR and the coded quantization parameter of the left visual point image of the A quality point of table 1 " Altmoabit " test pattern and the right visual point image correspondence of five different quality points

Y-PSNR and the coded quantization parameter of the left visual point image of the A quality point of table 2 " Doorflowers " test pattern and the right visual point image correspondence of five different quality points

The objective evaluation value of the stereo-picture that the right visual point image by the left visual point image of A quality point and five different quality points of table 3 " Altmoabit " test pattern constitutes

The objective evaluation value of the stereo-picture that the right visual point image by the left visual point image of A quality point and five different quality points of table 4 " Doorflowers " test pattern constitutes

The objective score value of the stereo-picture that the right visual point image by the left visual point image of A quality point and five different quality points of table 5 " Altmoabit " test pattern constitutes

The objective score value of the stereo-picture that the right visual point image by the left visual point image of A quality point and five different quality points of table 6 " Doorflowers " constitutes

Claims (5)

1. the objective analysis method of the minimum discernable change step of a stereo-picture is characterized in that may further comprise the steps:

1. make S _OrgUndistorted stereo-picture for original makes S _DisFor the stereo-picture of distortion to be evaluated, with S _OrgLeft visual point image be designated as L _Org, with S _OrgRight visual point image be designated as R _Org, with S _DisLeft visual point image be designated as L _Dis, with S _DisRight visual point image be designated as R _Dis

2. to L _Org, R _Org, L _DisAnd R _Dis4 width of cloth images are implemented singular value decomposition respectively, obtain L respectively _Org, R _Org, L _DisAnd R _DisEach self-corresponding singular value vector of 4 width of cloth images is with L _OrgThe singular value vector be designated as

With R _OrgThe singular value vector be designated as With L _DisThe singular value vector be designated as

With R _DisThe singular value vector be designated as

Wherein, the dimension of each singular value vector is m, and m=min (M, N), min () is for getting minimum value function, the horizontal size size of M presentation video, the vertical dimension size of N presentation video;

3. calculate L _OrgThe singular value vector

With L _DisThe singular value vector

The absolute difference vector, be designated as X ^L, With X ^LAs L _DisCharacteristic vector, calculate R _OrgThe singular value vector

With R _DisThe singular value vector

The absolute difference vector, be designated as X ^R,

With X ^RAs R _DisCharacteristic vector, wherein, " || " is the symbol that takes absolute value;

4. to L _DisCharacteristic vector X ^LAnd R _DisCharacteristic vector X ^RCarry out linear weighted function, obtain S _DisCharacteristic vector, be designated as X, X=w _LX ^L+ w _RX ^R, wherein, w _LExpression L _DisWeights proportion, w _RExpression R _DisWeights proportion, w _L+ w _R=1;

5. adopt n undistorted stereo-picture, set up its distortion stereo-picture set under the different distortion levels of coding distortion type H.264, this distortion stereo-picture set comprises the stereo-picture of several distortions, utilizes the subjective quality evaluation method to obtain the average subjective scoring difference of the stereo-picture of every width of cloth distortion in the set of distortion stereo-picture respectively, is designated as DMOS, DMOS=100-MOS, wherein, MOS represents the subjective scoring average, DMOS ∈ [0,100], n 〉=1;

6. adopt and calculating S _DisThe identical method of characteristic vector X, the different weights proportion combined feature vectors of the stereo-picture of every width of cloth distortion during the calculated distortion stereo-picture is gathered respectively, j kind weights proportion combined feature vector for the stereo-picture of i width of cloth distortion in the set of distortion stereo-picture is designated as X with it _{I, j}, wherein, 1≤i≤n', 1≤j≤m', n' represent the width of cloth number of the stereo-picture of the distortion that comprises in the distortion stereo-picture set, m' represents the kind number of all weights proportions combinations;

7. adopt support vector regression that the identical weights proportion combined feature vector of the stereo-picture of distortions all in the set of distortion stereo-picture is trained, obtain different weights than the support vector regression training pattern of recombination, for the support vector regression training pattern of j kind weights than recombination, it is designated as f _j(X _Inp), wherein, f _j() is that j kind weights are than the function representation form of the regression function of recombination, X _InpExpress support for the input vector of vector regression training pattern;

8. from n undistorted stereo-picture, choose a width of cloth stereo-picture arbitrarily, appoint then and get a coded quantization parameter the left visual point image of this stereo-picture is encoded, and adopt a plurality of different coded quantization parameters that the right visual point image of this stereo-picture is encoded, utilize support vector regression training pattern that training obtains that several test patterns that the width of cloth left side visual point image that obtained by coding and several right visual point images constitute are tested, but calculate the minimum discernable change step of human eye perception stereoscopic vision right visual point image in the test pattern when changing.

2. the objective analysis method of the minimum discernable change step of a kind of stereo-picture according to claim 1 is characterized in that described step detailed process 2. is:

2.-1, with size be the L of M * N _OrgBe expressed as the two-dimensional matrix of M * N dimension, be designated as By the two-dimensional matrix of singular value decomposition with M * N dimension

Be expressed as Wherein,

The orthogonal matrix of expression M * M dimension,

The orthogonal matrix of expression N * N dimension,

Expression

Transposed matrix,

The diagonal matrix of expression M * N dimension;

2.-2, the diagonal matrix that M * N is tieed up

Diagonal element as the two-dimensional matrix of M * N dimension

Singular value, from the two-dimensional matrix of M * N dimension

Singular value in take out the singular value formation L of m non-zero _OrgThe singular value vector, be designated as

Wherein, and m=min (M, N), min () is for getting minimum value function;

2.-3, to R _Org, L _DisAnd R _DisAdopt with step 2.-1 to 2.-2 identical operations, obtain R _Org, L _DisAnd R _DisThe singular value vector, be designated as respectively

With

3. the objective analysis method of the minimum discernable change step of a kind of stereo-picture according to claim 1 and 2 is characterized in that described step detailed process 7. is:

7.-1, with the identical weights proportion combined feature vector of the stereo-picture of distortions all in the distortion stereo-picture set and average subjective scoring difference as the set of training sample data, j kind weights are designated as Ω than the training sample data set of recombination _{Q, j}, { X _{K, j}, DMOS _k∈ Ω _{Q, j}, wherein, 1≤j≤m', m' represent the kind number of all weights proportion combinations, q represents that j kind weights are than the training sample data set omega of recombination _{Q, j}In the width of cloth number of stereo-picture of the distortion that comprises, X _{K, j}Represent that j kind weights are than the training sample data set omega of recombination _{Q, j}In the characteristic vector of stereo-picture of k width of cloth distortion, DMOS _kRepresent that j kind weights are than the training sample data set omega of recombination _{Q, j}In the average subjective scoring difference of stereo-picture of k width of cloth distortion, 1≤k≤q;

7.-2, structure X _{K, j}Regression function f _j(X _{K, j}),

Wherein, f _j() be j kind weights than the function representation form of the regression function of recombination, w is weight vector, w ^TBe the transposed matrix of w, b is bias term,

Represent that j kind weights are than the training sample data set omega of recombination _{Q, j}In the characteristic vector X of stereo-picture of k width of cloth distortion _{K, j}Linear function, D (X _{K, j}, X _{L, j}) be the kernel function in the support vector regression,

X _{L, j}Be that j kind weights are than the training sample data set omega of recombination _{Q, j}In the characteristic vector of stereo-picture of l width of cloth distortion, γ is nuclear parameter, is used for the scope of reflection input sample value, the scope of sample value is more big, and the γ value is also just more big, and exp () expression is the exponential function at the end with e, e=2.71828183, " || || " for asking the Euclidean distance symbol;

7.-3, adopt support vector regression to the training sample data set omega of j kind weights than recombination _{Q, j}In the characteristic vector of stereo-picture of all distortion train, make error minimum between the regression function value that obtains through training and the average subjective scoring difference, match obtains the weight vector w of optimum ^OptBias term b with optimum ^Opt, with the weight vector w of optimum ^OptBias term b with optimum ^OptCombination be designated as (w ^Opt, b ^Opt), $(w^{\mathrm{opt}},b^{\mathrm{opt}})=\underset{(w,b)\&Element;\mathrm{\&Psi;}}{\arg \min }\underset{k=1}{\overset{q}{\mathrm{\&Sigma;}}}{(f_j\left(X_{k,j}\right)-{\mathrm{DMOS}}_k)}^2,$ Utilize optimum weight vector w ^OptBias term b with optimum ^OptConstruct j kind weights than the support vector regression training pattern of recombination, be designated as f _j(X _Inp),

Wherein, Ψ represents the training sample data set omega of j kind weights than recombination _{Q, j}In the set of combination of the characteristic vector of stereo-picture of all distortion all weight vector of training and bias term, Expression minimizes probability density function, X _InpExpress support for the input vector of vector regression training pattern, (w ^Opt) ^TBe w ^OptTransposed matrix,

Express support for the input vector X of vector regression training pattern _InpLinear function.

4. the objective analysis method of the minimum discernable change step of a kind of stereo-picture according to claim 3 is characterized in that described step detailed process 8. is:

8.-1, from n undistorted stereo-picture, choose a width of cloth stereo-picture arbitrarily, appoint and get a coded quantization parameter as the basic coding quantization parameter of the left visual point image of this stereo-picture, be designated as QP _L, will adopt QP _LThe encode left visual point image that obtains of the left visual point image of this stereo-picture is defined as the left visual point image of A quality point, is designated as L _A

8.-2, obtain five values more than or equal to QP _LAnd the coded quantization parameter that value has nothing in common with each other is as the basic coding quantization parameter of the right visual point image of this stereo-picture, adopt these five basic coding quantization parameters respectively the right visual point image of this stereo-picture to be encoded, obtain the right visual point image that five width of cloth quality have nothing in common with each other, be defined as the right visual point image of A quality point, the right visual point image of B quality point, the right visual point image of C quality point, the right visual point image of D quality point, the right visual point image of E quality point respectively, be designated as R respectively _A, R _B, R _C, R _D, R _E, wherein, with the right visual point image R of A quality point _AThe basic coding quantization parameter that adopts during coding is designated as QP _RA, with the right visual point image R of B quality point _BThe basic coding quantization parameter that adopts during coding is designated as QP _RB, with the right visual point image R of C quality point _CThe basic coding quantization parameter that adopts during coding is designated as QP _RC, with the right visual point image R of D quality point _DThe basic coding quantization parameter that adopts during coding is designated as QP _RD, with the right visual point image R of E quality point _EThe basic coding quantization parameter that adopts during coding is designated as QP _RE

8.-3, with the left visual point image L of A quality point _ARight visual point image R with the A quality point _AThe stereo-picture that constitutes is as the 1st width of cloth test pattern, and is designated as S _AALeft visual point image L with the A quality point _ARight visual point image R with the B quality point _BThe stereo-picture that constitutes is as the 2nd width of cloth test pattern, and is designated as S _ABLeft visual point image L with the A quality point _ARight visual point image R with the C quality point _CThe stereo-picture that constitutes is as the 3rd width of cloth test pattern, and is designated as S _ACLeft visual point image L with the A quality point _ARight visual point image R with the D quality point _DThe stereo-picture that constitutes is as the 4th width of cloth test pattern, and is designated as S _ADLeft visual point image L with the A quality point _ARight visual point image R with the E quality point _EThe stereo-picture that constitutes is as the 5th width of cloth test pattern, and is designated as S _AE

8.-4, adopt and calculate S _DisThe identical method of characteristic vector X, calculate S _AAAdopt different weights proportion combined feature vectors, with S _AAAdopt j kind weights proportion combined feature vector to be designated as X _{A, j}, wherein, 1≤j≤m', m' represent the kind number of all weights proportion combinations;

8.-5, according to the support vector regression training pattern, the prediction S _AAAdopt the evaluating objective quality predicted value of all different weights proportion combined feature vectors, for S _AAAdopt j kind weights proportion combined feature vector X _{A, j}, with X _{A, j}As the input vector of j kind weights than the support vector regression training pattern of recombination, prediction obtains X _{A, j}The evaluating objective quality predicted value, be designated as Q _j, Q _j=f _j(X _{A, j}),

Wherein, w ^OptBe the weight vector of optimum, (w ^Opt) ^TBe w ^OptTransposed matrix, b ^OptBe the bias term of optimum,

Expression X _{A, j}Linear function;

8.-6, with S _AAAdopt the mean value of evaluating objective quality predicted value of all different weights proportion combined feature vectors as S _AAThe objective evaluation value, be designated as Score _A,

8.-7,8.-4 employing and step to 8.-6 identical operations, obtain S _AB, S _AC, S _ADAnd S _AEThe objective evaluation value, be designated as Score respectively _B, Score _C, Score _DAnd Score _E

8.-8, calculate S _AA, S _AB, S _AC, S _ADAnd S _AEObjective score value, be designated as D respectively ₁, D ₂, D ₃, D ₄And D ₅, D ₁=0, $D_2=\frac{{\mathrm{Score}}_B-{\mathrm{Score}}_A}{{\mathrm{QP}}_{\mathrm{RB}}-{\mathrm{QP}}_{\mathrm{RA}}},$ $D_3=\frac{{\mathrm{Score}}_C-{\mathrm{Score}}_B}{{\mathrm{QP}}_{\mathrm{RC}}-{\mathrm{QP}}_{\mathrm{RB}}},$ $D_4=\frac{{\mathrm{Score}}_D-{\mathrm{Score}}_C}{{\mathrm{QP}}_{\mathrm{RD}}-{\mathrm{QP}}_{\mathrm{RC}}},$ $D_5=\frac{{\mathrm{Score}}_E-{\mathrm{Score}}_D}{{\mathrm{QP}}_{\mathrm{RE}}-{\mathrm{QP}}_{\mathrm{RD}}};$

8.-9, from { D _J'| the maximum objective score value of the value of finding out among 1≤j'≤5} is designated as D _Max, with D _MaxThe quality point of corresponding right visual point image is designated as q, the stereo-picture that is constituted by the left visual point image of the right visual point image of q quality point and A quality point then, and the picture quality difference that comes with the English alphabet order between the stereo-picture of the corresponding right visual point image of each quality point and the left visual point image formation of A quality point before the q quality point is distinguishable, the difference of the Y-PSNR of the Y-PSNR of the right visual point image of definition q quality point and the right visual point image of A quality point is as adopting QP _LHuman eye was to the discernable change step of appreciable minimum of asymmetric stereo image coding when left visual point image was encoded.

5. the objective analysis method of the minimum discernable change step of a kind of stereo-picture according to claim 4, it is characterized in that during described step 6. in the process of the different weights proportion combined feature vectors of the stereo-picture of calculated distortion, j kind weights being designated as (w than recombination _L', w _R'), w _R'=w ₀+ (j-1) * and Δ w, w _L'=1-w _R', wherein, w _L' the weights proportion of left visual point image of stereo-picture of expression distortion, w _R' the weights proportion of right visual point image of stereo-picture of expression distortion, w ₀=0.55, Δ w=0.05,1≤j≤m', m' represent the kind number of all weights proportion combinations.

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