CN109191428B - Masking texture feature-based full-reference image quality evaluation method - Google Patents

Masking texture feature-based full-reference image quality evaluation method Download PDF

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CN109191428B
CN109191428B CN201810834955.5A CN201810834955A CN109191428B CN 109191428 B CN109191428 B CN 109191428B CN 201810834955 A CN201810834955 A CN 201810834955A CN 109191428 B CN109191428 B CN 109191428B
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郑元林
王玮
唐梽森
廖开阳
于淼淼
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Xian University of Technology
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Abstract

The invention discloses a full-reference image quality evaluation method based on masking texture features, which belongs to the technical field of image processing and image quality evaluation, and comprises the steps of firstly carrying out color space conversion on a reference image and a distorted image, secondly extracting gradient amplitude and gradient direction features of the reference image and the distorted image and calculating image gradient information similarity, then calculating texture feature similarity and chromatic aberration, respectively counting the mean value and standard deviation of the texture feature similarity and the chromatic aberration to form a 6-D feature vector, establishing a regression model according to a random forest to fuse the feature vector and a subjective MOS value, and carrying out training; and finally, extracting the 6-D characteristic vector of the image to be detected, inputting the vector into the trained regression model, and finishing objective image quality evaluation. The evaluation method disclosed by the invention adopts three different similarity characteristics, uses a random forest to establish a regression model, realizes high-precision objective evaluation of the quality of the full-reference image, and can keep higher consistency with the visual characteristics of human eyes.

Description

Masking texture feature-based full-reference image quality evaluation method
Technical Field
The invention belongs to the technical field of image processing and image quality evaluation, and relates to a full-reference image quality evaluation method based on masking texture features.
Background
As the big data age has come, more and more images are shared on the network. Digital images are used as important carriers for people to acquire information and communicate, and the life style of people is gradually changed. With the large increase of the data size, a great challenge is brought, and the image may be distorted to a certain degree in the processes of acquisition, storage, transmission and processing. Therefore, how to effectively process and transmit images and accurately evaluate the image quality has become a problem to be researched.
In recent years, the full-reference image quality evaluation algorithm and the corresponding device are widely applied to various image processing systems to optimize parameters, so the full-reference image quality evaluation becomes a research hotspot. Most of the existing full-reference image quality evaluation methods adopt a framework based on Human Visual Systems (HVS) as a principle, and w.zhou et al propose an image evaluation method: firstly, respectively extracting three indexes of brightness information, contrast information, structure information and the like of a reference image and a corresponding distorted image; secondly, calculating the similarity of the three indexes to obtain the brightness similarity, the contrast similarity and the structural similarity; and finally, averagely weighting the three similarity characteristics to obtain the quality score of the distorted image, and giving visual characteristic weight according to the image content on the premise of the theory. In addition, there are some methods for extracting a global feature from the whole image in the spatial domain to perform quality evaluation, but this method cannot be used for evaluating a color image.
At present, in some researches, frequency domain characteristics are adopted to describe image structure information and further improve an image quality evaluation model, but most of image quality evaluation methods based on characteristic similarity calculation cannot accurately reflect human eye vision masking effect, and influence of complex factors such as physiology, psychology and the like on human eye vision is ignored, so that the evaluation result precision is low.
Disclosure of Invention
The invention aims to provide a masking texture feature-based full-reference image quality evaluation method, which solves the problems that the existing evaluation method cannot accurately reflect the masking effect of human vision and neglects the influence of complex factors such as physiology, psychology and the like on the human vision.
The technical scheme adopted by the invention is that the method for evaluating the quality of the full-reference image based on the masking texture features comprises the following steps:
step 1, converting a reference image and a distorted image in a database from an RGB color space to an Lab color space, and separating color information and brightness information of the image;
step 2, respectively extracting the gradient amplitude and gradient direction characteristics of the reference image and the distorted image in the L channel according to the Lab color space obtained in the step 1, and calculating the gradient amplitude similarity and the gradient direction similarity;
step 3, after the step 1 is finished, Laws texture characteristics of the L channel in the reference image and the distorted image are sequentially extracted, and the texture similarity mean value and the standard deviation of the reference image and the distorted image are counted;
step 4, calculating the color differences of the reference image and the distorted image in three channels L, a and b according to the Lab color space obtained in the step 1, and counting the mean value and the standard deviation of the color differences;
and 5, after the step 2, the step 3 and the step 4 are finished, fusing the obtained gradient amplitude similarity, gradient direction similarity, texture similarity mean value and standard deviation and feature similarity color difference mean value and standard deviation in a regression model through a random forest, inputting the subjective evaluation score MOS value into the regression model for training, and directly using the trained model for accurately predicting the quality of the image to be evaluated.
Yet another feature of the present invention is that,
the specific process of step 1 is as follows:
color space conversion is performed on the reference image and the distorted image in the database according to formulas 1-3, and conversion is performed from an RGB color space to a Lab color space:
Figure GDA0003043894510000031
Figure GDA0003043894510000032
Figure GDA0003043894510000033
wherein R, G and B respectively represent three channels of a color image, X, Y and Z respectively represent tristimulus values of colors, and X0＝0.9505，Y0＝1.000，Z01.0890 is D65Tristimulus value, L, under illumination conditions*Representing the lightness channel after color space conversion, a*And b*Respectively representing the chrominance channels after color space conversion; the RGB color space is calculated by formula 1, formula 2 and formula 3 to obtain Lab color space, the size of the image after color space conversion is the same as the size of the image before color space conversion, and the separation of the luminance information L channel of the image from the chrominance information a and b channels is realized.
The specific process of step 2 is as follows:
step 2.1, performing convolution operation on the reference image and the distorted image respectively by using a Prewitt operator with 3 × 3 window horizontal and vertical components, and extracting the features of gradient amplitude and gradient direction:
for an image f (x), x represents the position of a pixel point, and the method of convolving the image is shown in formula 4:
Figure GDA0003043894510000041
in the formula, Gx(x) Representing horizontal gradient amplitude values, Gy(x) Representing a vertical gradient magnitude value;
step 2.2, after step 2.1, calculating the gradient amplitude value gm (x) and the gradient direction value θ (x) of the reference image and the distorted image according to the formulas 5 and 6, respectively, wherein the specific calculation method is as follows:
Figure GDA0003043894510000042
Figure GDA0003043894510000043
step 2.3 after step 2.2, the gradient amplitude similarity of the reference image and the distorted image is calculated according to formula 7 and formula 8 respectively
Figure GDA0003043894510000044
And gradient direction similarity Sor(x) The specific calculation method is as follows:
Figure GDA0003043894510000045
Figure GDA0003043894510000046
in formula 7, m and n respectively represent the width and height of the image, x represents the position of the pixel point, and Ir(x) And Id(x) Respectively representing a reference image and a distorted image; in the formula 8, θrAnd thetadRespectively representing the gradient directions of the reference image and the distorted image, C1＝1。
The specific process of step 3 is as follows:
step 3.1, extracting texture features, namely performing convolution operation on the image by adopting four two-dimensional Laws filters, wherein the four two-dimensional Laws filters are shown as a formula 9:
Figure GDA0003043894510000051
for an image f (x), x represents the position of the pixel point, the convolution operation is performed on the image and the four templates in the formula 9 respectively, and the maximum value is taken, and the specific form is shown in the formula 10:
te＝max(f(x)*i),i＝(a),(b),(c),(d) (10)
step 3.2, after the step 3.1, calculating the texture similarity of the reference image and the distorted image, wherein the specific calculation mode is as follows:
Figure GDA0003043894510000052
in equation 11, terAnd tedTexture features representing the reference image and the distorted image, respectively, C2＝100；
Step 3.3 after step 3.2, the mean of the convolution results is counted
Figure GDA0003043894510000053
And standard deviation of
Figure GDA0003043894510000054
The specific statistical form is as follows: (ii) a
Figure GDA0003043894510000055
In the formula 12, the first and second groups of the formula,
Figure GDA0003043894510000056
the mean value of the similarity of the textures is represented,
Figure GDA0003043894510000057
the texture similarity standard deviation is represented, and n represents the total number of pixel points.
The specific process of step 4 is as follows:
step 4.1, according to the Lab color space obtained in step 1, the color difference values Δ E of the reference image and the distorted image under the three channels L, a, and b are respectively calculated, as shown in formula 13:
Figure GDA0003043894510000061
in the formula 13, the first and second groups,
Figure GDA0003043894510000062
respectively representing the values of the three channels in the Lab color space, wherein the subscript r and the subscript d respectively represent the reference image and the distorted image;
step 4.2 statistics of the mean of the color differences
Figure GDA0003043894510000063
And standard deviation of
Figure GDA0003043894510000064
As shown in equations 14 and 15:
Figure GDA0003043894510000065
Figure GDA0003043894510000066
in the formula, m and n respectively represent the width and the height of the color difference diagram, and (i and j) represent the position of a two-dimensional plane where a pixel point is located.
The specific process of step 5 is as follows:
step 5.1 six similarity features to be obtained
Figure GDA0003043894510000067
Sor，
Figure GDA0003043894510000068
And
Figure GDA0003043894510000069
and subjective average score MOS values of the distorted images in the database are input into a regression model established by a random forest together for training, the number ntree of decision trees in the model is set to be 500, and the number mtry of number node preselected variables is set to be 2;
and 5.2, extracting similarity characteristics from one or more distortion images to be detected and reference images corresponding to the distortion images according to the steps 2, 3 and 4 by using the trained regression model, inputting the similarity characteristics into the trained random forest regression model to obtain an output prediction quality score, and finishing the evaluation of the quality of the distortion images.
The method has the advantages that three different similarity characteristics of the image are extracted from a large public database based on the masking texture characteristic full-reference image quality evaluation method, and the mean value and standard deviation of the similarity characteristics are counted to describe image information in a mutually complementary manner, so that the problem that the consistency between the traditional characteristics and the subjective perception of human eyes is low is solved; a regression model can be established according to Random Forests (RF), the mean value and the variance of each similarity feature are fused, and learning and prediction are carried out by combining subjective score MOS values, so that the robustness of the model is improved, and the application universality is increased; when in use, the method can greatly improve the image quality prediction precision and has high consistency with a human visual system.
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Fig. 1 is a frame diagram of a masking texture feature-based full-reference image quality evaluation method according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The method for evaluating the quality of the full-reference image based on the masking texture features, as shown in fig. 1, can be divided into two parts, which are respectively: establishing an RF model and predicting image quality evaluation: the method comprises the steps that an RF model building part is used for building a regression model by using random forest RF, wherein processing objects are a reference image and a distorted image in an image database, the mean value and the variance of three similarity characteristics in the method are extracted, and the regression model is built by combining subjective MOS values in the database;
and the prediction part of the image quality evaluation calculates the gradient amplitude similarity, the gradient direction similarity, the texture similarity mean value, the texture similarity standard deviation, the color difference mean value and the color difference standard deviation of the distorted image and the corresponding reference image, molds the three similarity features into a 6-D feature vector, and inputs the 6-D feature vector into the RF regression model as an input value, thereby predicting the quality of the distorted image and finishing the evaluation of the image quality.
The specific operation process comprises the following steps:
step 1, converting the reference image and the distorted image in the database from an RGB color space to an Lab color space, and separating the color information and the brightness information of the image:
color space conversion is performed on the reference image and the distorted image in the database according to formulas 1-3, and conversion is performed from an RGB color space to a Lab color space:
Figure GDA0003043894510000081
Figure GDA0003043894510000082
Figure GDA0003043894510000083
wherein R, G and B respectively represent three channels of a color image, X, Y and Z respectively represent tristimulus values of colors, and X0＝0.9505，Y0＝1.000，Z01.0890 is D65Tristimulus value, L, under illumination conditions*Representing the lightness channel after color space conversion, a*And b*Respectively representing the chrominance channels after color space conversion; calculating the RGB color space by a formula 1, a formula 2 and a formula 3 to obtain a Lab color space, wherein the size of the image after color space conversion is the same as that of the image before color space conversion, so that the separation of a brightness information L channel and chrominance information a and b channels of the image is realized;
step 2, according to the Lab color space obtained in the step 1, respectively extracting the gradient amplitude and gradient direction characteristics of the reference image and the distorted image in the L channel, and calculating the gradient amplitude similarity and the gradient direction similarity:
step 2.1, performing convolution operation on the reference image and the distorted image respectively by using a Prewitt operator with 3 × 3 window horizontal and vertical components, and extracting the features of gradient amplitude and gradient direction:
for an image f (x), x represents the position of a pixel point, and the method of convolving the image is shown in formula 4:
Figure GDA0003043894510000091
in the formula, Gx(x) Representing horizontal gradient amplitude values, Gy(x) Representing a vertical gradient magnitude value;
step 2.2, after step 2.1, calculating the gradient amplitude value gm (x) and the gradient direction value θ (x) of the reference image and the distorted image according to the formulas 5 and 6, respectively, wherein the specific calculation method is as follows:
Figure GDA0003043894510000092
Figure GDA0003043894510000093
step 2.3, after the step 2.2 is finished, respectively calculating the gradient amplitude similarity of the reference image and the distorted image according to a formula 7 and a formula 8
Figure GDA0003043894510000094
And gradient direction similarity Sor(x) The specific calculation method is as follows:
Figure GDA0003043894510000095
Figure GDA0003043894510000096
in formula 7, m and n respectively represent the width and height of the image, x represents the position of the pixel point, and Ir(x) And Id(x) Respectively representing a reference image and a distorted image; in the formula 8, θrAnd thetadRespectively representing the gradient directions of the reference image and the distorted image, C 11, to stabilize equation 8, avoid the denominator appearing as zero.
And 3, after the step 1 is finished, sequentially extracting Laws texture features of the L channel in the reference image and the distorted image, and counting the mean value and the standard deviation of the texture similarity of the reference image and the distorted image:
step 3.1, extracting texture features, namely performing convolution operation on the image by adopting four two-dimensional Laws filters and taking the maximum value, wherein the four two-dimensional Laws filters are shown as a formula 9:
Figure GDA0003043894510000101
for an image f (x), x represents the position of the pixel point, the convolution operation is performed on the image and the four templates in the formula 9 respectively, and the maximum value is taken, and the specific form is shown in the formula 10:
te＝max(f(x)*i),i＝(a),(b),(c),(d) (10)
step 3.2, after the step 3.1, calculating the texture similarity of the reference image and the distorted image, wherein the specific calculation mode is as follows:
Figure GDA0003043894510000102
in equation 11, terAnd tedTexture features representing the reference image and the distorted image, respectively, C2100, which is used for stabilizing formula 11 and avoiding the phenomenon that the denominator is zero;
step 3.3 after step 3.2, the mean of the convolution results is counted
Figure GDA0003043894510000103
And standard deviation of
Figure GDA0003043894510000104
The specific statistical form is as follows:
Figure GDA0003043894510000105
in the formula 12, the first and second groups of the formula,
Figure GDA0003043894510000106
the mean value of the similarity of the textures is represented,
Figure GDA0003043894510000107
the texture similarity standard deviation is represented, and n represents the total number of pixel points.
Step 4, calculating the color differences of the reference image and the distorted image in three channels L, a and b according to the Lab color space obtained in the step 1, and counting the mean value and the standard deviation of the color differences;
step 4.1, according to the Lab color space obtained in step 1, the color difference values Δ E of the reference image and the distorted image under the three channels L, a, and b are respectively calculated, as shown in formula 13:
Figure GDA0003043894510000111
in the formula 13, the first and second groups,
Figure GDA0003043894510000112
respectively representing the values of the three channels in the Lab color space, wherein the subscript r and the subscript d respectively represent the reference image and the distorted image;
step 4.2 statistics of the mean of the color differences
Figure GDA0003043894510000113
And standard deviation of
Figure GDA0003043894510000114
As shown in equations 14 and 15:
Figure GDA0003043894510000115
Figure GDA0003043894510000116
in the formulas 14 and 15, m and n respectively represent the width and height of the color difference graph, and (i and j) represent the position of a two-dimensional plane where a pixel point is located;
and 5, after the step 2, the step 3 and the step 4 are finished, fusing the obtained gradient amplitude similarity, gradient direction similarity, texture similarity mean value and standard deviation and feature similarity color difference mean value and standard deviation in a regression model through a random forest, inputting the subjective evaluation score MOS value into the regression model for training, and directly using the trained model for accurately predicting the quality of the image to be evaluated:
step 5.1 six similarity features to be obtained
Figure GDA0003043894510000117
Sor，
Figure GDA0003043894510000118
And
Figure GDA0003043894510000119
and subjective average score MOS values of the distorted images in the database are input into a regression model established by a random forest together for training, the number ntree of decision trees in the model is set to be 500, and the number mtry of number node preselected variables is set to be 2;
and 5.2, extracting similarity characteristics from one or more distortion images to be detected and reference images corresponding to the distortion images according to the steps 2, 3 and 4 by using the trained regression model, inputting the similarity characteristics into the trained random forest regression model to obtain an output prediction quality score, and finishing the evaluation of the quality of the distortion images.
The invention relates to a method for evaluating the quality of a full-reference image based on masking texture characteristics, which comprises the steps of firstly, carrying out color space conversion on a reference image and a distorted image in a database; secondly, extracting spatial gradient and frequency domain phase characteristics of the reference image and the distorted image to calculate the global maximum structural characteristic similarity; then, calculating the similarity of the frequency domain texture and the spatial frequency characteristic, the similarity of the spatial color characteristic, and combining the global maximum structural characteristic similarity to form a 6-D characteristic vector; then, establishing a regression model for training through random forest RF in combination with the feature vector and the MOS value; and finally, extracting the 6-D characteristic vector of the image to be detected, taking the vector as an input value of a random forest RF regression model, and carrying out high-precision prediction on the quality of the image to be detected so as to evaluate the image quality.
The method for evaluating the quality of the full-reference image based on the masking texture features fully utilizes the mean value and the variance of three similarity features consistent with the visual characteristics of human eyes, can establish a random forest RF regression model according to the reference image and the distorted image in the database to fuse the similarity features, and carries out training and prediction, thereby evaluating the quality of the predicted image with high precision and keeping high consistency with human eye identification.

Claims (6)

1. The method for evaluating the quality of the full-reference image based on the masking texture features is characterized by comprising the following steps of:
step 1, converting a reference image and a distorted image in a database from an RGB color space to an Lab color space, and separating color information and brightness information of the image;
step 2, respectively extracting the gradient amplitude and gradient direction characteristics of the reference image and the distorted image in the L channel according to the Lab color space obtained in the step 1, and calculating the gradient amplitude similarity and the gradient direction similarity;
step 3, after the step 1 is finished, Laws texture characteristics of the L channel in the reference image and the distorted image are sequentially extracted, and the texture similarity mean value and the standard deviation of the reference image and the distorted image are counted;
step 4, calculating the color differences of the reference image and the distorted image in three channels L, a and b according to the Lab color space obtained in the step 1, and counting the mean value and the standard deviation of the color differences;
and 5, after the step 2, the step 3 and the step 4 are finished, fusing the obtained gradient amplitude similarity, gradient direction similarity, texture similarity mean value and standard deviation and feature similarity color difference mean value and standard deviation in a regression model through a random forest, inputting the subjective evaluation score MOS value into the regression model for training, and directly using the trained model for accurately predicting the quality of the image to be evaluated.
2. The method for evaluating the quality of the fully-referenced image based on the masking texture features as claimed in claim 1, wherein the specific process of the step 1 is as follows:
color space conversion is performed on the reference image and the distorted image in the database according to formulas 1-3, and conversion is performed from an RGB color space to a Lab color space:
Figure FDA0003043894500000011
Figure FDA0003043894500000021
Figure FDA0003043894500000022
wherein R, G and B respectively represent three channels of a color image, X, Y and Z respectively represent tristimulus values of colors, and X0＝0.9505，Y0＝1.000，Z01.0890 is D65Tristimulus value, L, under illumination conditions*Representing the lightness channel after color space conversion, a*And b*Respectively representing the chrominance channels after color space conversion; the RGB color space is calculated by formula 1, formula 2 and formula 3 to obtain Lab color space, the size of the image after color space conversion is the same as the size of the image before color space conversion, and the separation of the luminance information L channel of the image from the chrominance information a and b channels is realized.
3. The method for evaluating the quality of the fully-referenced image based on the masking texture features as claimed in claim 1, wherein the specific process of the step 2 is as follows:
step 2.1, performing convolution operation on the reference image and the distorted image respectively by using a Prewitt operator with 3 × 3 window horizontal and vertical components, and extracting the features of gradient amplitude and gradient direction:
for an image f (x), x represents the position of a pixel point, and the method of convolving the image is shown in formula 4:
Figure FDA0003043894500000023
in the formula, Gx(x) Representing horizontal gradient amplitude values, Gy(x) Representing a vertical gradient magnitude value;
step 2.2, after step 2.1, calculating the gradient amplitude value gm (x) and the gradient direction value θ (x) of the reference image and the distorted image according to the formulas 5 and 6, respectively, wherein the specific calculation method is as follows:
Figure FDA0003043894500000031
Figure FDA0003043894500000032
step 2.3 after step 2.2, the gradient amplitude similarity of the reference image and the distorted image is calculated according to formula 7 and formula 8 respectively
Figure FDA0003043894500000033
And gradient direction similarity Sor(x) The specific calculation method is as follows:
Figure FDA0003043894500000034
Figure FDA0003043894500000035
in formula 7, m and n respectively represent the width and height of the image, x represents the position of the pixel point, and Ir(x) And Id(x) Respectively representing a reference image and a distorted image; in the formula 8, θrAnd thetadRespectively representing the gradient directions of the reference image and the distorted image, C1＝1。
4. The method for evaluating the quality of the fully-referenced image based on the masking texture features as claimed in claim 1, wherein the specific process of the step 3 is as follows:
step 3.1, extracting texture features, namely performing convolution operation on the image by adopting four two-dimensional Laws filters, wherein the four two-dimensional Laws filters are shown as a formula 9:
Figure FDA0003043894500000036
for an image f (x), x represents the position of the pixel point, the convolution operation is performed on the image and the four templates in the formula 9 respectively, and the maximum value is taken, and the specific form is shown in the formula 10:
te＝max(f(x)*i),i＝(a),(b),(c),(d) (10)
step 3.2, after the step 3.1, calculating the texture similarity of the reference image and the distorted image, wherein the specific calculation mode is as follows:
Figure FDA0003043894500000041
in equation 11, terAnd tedTexture features representing the reference image and the distorted image, respectively, C2＝100；
Step 3.3 after step 3.2, the mean of the convolution results is counted
Figure FDA0003043894500000042
And standard deviation of
Figure FDA0003043894500000043
The specific statistical form is as follows:
Figure FDA0003043894500000044
in the formula 12, the first and second groups of the formula,
Figure FDA0003043894500000045
the mean value of the similarity of the textures is represented,
Figure FDA0003043894500000046
the texture similarity standard deviation is represented, and n represents the total number of pixel points.
5. The method for evaluating the quality of the fully-referenced image based on the masking texture features as claimed in claim 1, wherein the specific process of the step 4 is as follows:
step 4.1, according to the Lab color space obtained in step 1, the color difference values Δ E of the reference image and the distorted image under the three channels L, a, and b are respectively calculated, as shown in formula 13:
Figure FDA0003043894500000047
in the formula 13, the first and second groups,
Figure FDA0003043894500000048
respectively representing the values of the three channels in the Lab color space, wherein the subscript r and the subscript d respectively represent the reference image and the distorted image;
step 4.2 statistics of the mean of the color differences
Figure FDA0003043894500000051
And standard deviation of
Figure FDA0003043894500000052
As shown in equations 14 and 15:
Figure FDA0003043894500000053
Figure FDA0003043894500000054
in the formula, m and n respectively represent the width and the height of the color difference diagram, and (i and j) represent the position of a two-dimensional plane where a pixel point is located.
6. The method for evaluating the quality of the fully-referenced image based on the masking texture features as claimed in claim 1, wherein the specific process of the step 5 is as follows:
step 5.1 six similarity features to be obtained
Figure FDA0003043894500000055
Sor，
Figure FDA0003043894500000058
And
Figure FDA0003043894500000057
and subjective average score MOS values of the distorted images in the database are input into a regression model established by a random forest together for training, the number ntree of decision trees in the model is set to be 500, and the number mtry of number node preselected variables is set to be 2;
and 5.2, extracting similarity characteristics from one or more distortion images to be detected and reference images corresponding to the distortion images according to the steps 2, 3 and 4 by using the trained regression model, inputting the similarity characteristics into the trained random forest regression model to obtain an output prediction quality score, and finishing the evaluation of the quality of the distortion images.
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