CN109146856A - Picture quality assessment method, device, computer equipment and storage medium - Google Patents
Picture quality assessment method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The present invention relates to picture quality assessment method, device, computer equipment and storage medium, this method includes obtaining image；Clarity analysis is carried out to image, to obtain image definition score；Deep learning is carried out to image using deep learning model, and Score on Prediction is carried out to learning outcome, to obtain image quality score；Synthetic image articulation score and image quality score form evaluation result；Export evaluation result.The present invention is by first scoring to training image from illumination, size and deflection angle, training image is subjected to quality score, utilize the training set for the feature vector composition that training image extracts, the mass fraction of training image is trained adjustment to CNN sorter network, to obtain deep learning mode, when use, image is subjected to clarity scoring and deep learning model scores, synthesis is weighted to two scores again, obtain evaluation result, accuracy is determined to improve picture quality, improves the accuracy rate of true environment human face identification.
Description
Technical field
The present invention relates to image evaluation methods, more specifically refer to picture quality assessment method, device, computer equipment
And storage medium.
Background technique
With the continuous development of deep learning and artificial intelligence technology, face identification system is increasingly used in pacifying
Anti-, intelligence new retail, finance, subway, hotel, airport etc. need in the scene of authentication, and such as bank remotely opens an account, nobody
Super quotient is automatically performed that payment, access control system, subway brush face is paid, airport carries out testimony of a witness veritification etc. automatically by brush face.
Existing recognition of face is directly to obtain the face captured from camera to carry out recognition of face mostly, is directly captured
Picture contains many motion blurs or focuses unsharp face, and also many facial angles, scale and illumination is improper,
Recognition accuracy is leveraged, also face recognition application is caused to be a greater impact, although existing minority face identification system
Also there is the judgement of face picture quality, but be substantially and judged in terms of picture clarity, to facial angle, illumination, rotation
Turn, scale etc. is not assessed, while the judgement of clarity contains many subjective assessments, and face recognition accuracy rate is not
Height, therefore existing face picture Environmental Evaluation Model is poor to the evaluation effect of face picture quality.
Therefore, it is necessary to design a kind of new method, the accuracy for improving picture quality judgement is realized, greatly improve true
The accuracy rate of real environment human face identification.
Summary of the invention
It is an object of the invention to overcome the deficiencies of existing technologies, picture quality assessment method, device, computer are provided and set
Standby and storage medium.
To achieve the above object, the invention adopts the following technical scheme: picture quality assessment method, comprising:
Obtain image；
Clarity analysis is carried out to image, to obtain image definition score；
Deep learning is carried out to image using deep learning model, and Score on Prediction is carried out to learning outcome, to obtain figure
As mass fraction；
Synthetic image articulation score and image quality score form evaluation result；
Export evaluation result.
Its further technical solution are as follows: it is described that deep learning is carried out to image using deep learning model, to obtain image
Before mass fraction, further includes:
Obtain several training images；
Quality score is carried out to training image and is marked；
Training image after mark is trained, to obtain deep learning model.
Its further technical solution are as follows: the training image after described pair of mark is trained, to obtain deep learning model,
Include:
Whether the size of training of judgement image meets preset requirement；
If it is not, then carrying out size processing to training image, and enter next step, if so, into next step；
Several training image blocks are screened from training image；
The corresponding feature vector of training image blocks is extracted, and establishes training set；
Using training set and training image training CNN sorter network, to obtain deep learning model.
Its further technical solution are as follows: it is described that clarity analysis is carried out to image, to obtain image definition score, packet
It includes:
Energy gradient sharpness computation is carried out to image, to obtain calculated result；
The calculated result is normalized, to obtain image definition score.
Its further technical solution are as follows: it is described that deep learning is carried out to image using deep learning model, and study is tied
Fruit carries out Score on Prediction, to obtain image quality score, comprising:
Several testing image blocks are extracted to image；
Several testing image blocks are inputted into deep learning model, to extract the feature vector of several testing image blocks；
Predict to the feature vector of several testing image blocks the mass fraction of several testing image blocks；
The average value of the mass fraction of several testing image blocks is obtained, to obtain image quality score.
Its further technical solution are as follows: the synthetic image articulation score and image quality score form evaluation knot
Fruit, comprising:
Image definition score and image quality score are weighted summation according to specified weight, to form evaluation knot
Fruit.
The present invention also provides picture quality evaluation devices, comprising:
Image acquisition unit, for obtaining image；
Clarity analytical unit, for carrying out clarity analysis to image, to obtain image definition score；
Deep learning unit for carrying out deep learning to image using deep learning model, and carries out learning outcome
Score on Prediction, to obtain image quality score；
Comprehensive unit is used for synthetic image articulation score and image quality score, forms evaluation result；
As a result output unit, for exporting evaluation result.
Its further technical solution are as follows: the clarity analytical unit includes:
Sharpness computation subelement, for carrying out energy gradient sharpness computation to image, to obtain calculated result；
Normalizing subelement, for normalizing the calculated result, to obtain image definition score.
The present invention also provides a kind of computer equipment, including memory, processor and it is stored on the memory simultaneously
The computer program that can be run on the processor, the processor realize above-mentioned image when executing the computer program
Performance rating method.
The present invention also provides a kind of storage medium, the storage medium is stored with computer program, the computer journey
Sequence includes program instruction, and described program instruction makes the processor execute above-mentioned picture quality evaluation when being executed by a processor
Method.
Compared with the prior art, the invention has the advantages that: the present invention by first to training image from illumination, size and
Deflection angle scores, and training image is carried out quality score, utilizes the training for the feature vector composition that training image extracts
Collection, the mass fraction of training image is trained adjustment to CNN sorter network, to obtain deep learning mode, in use, will figure
It scores as carrying out clarity scoring and deep learning model, then synthesis is weighted to two scores, commented to obtain
Determine as a result, greatly improving the accuracy rate of true environment human face identification to improve the accuracy of picture quality judgement.
The invention will be further described in the following with reference to the drawings and specific embodiments.
Detailed description of the invention
Fig. 1 is the application scenario diagram for the picture quality assessment method that the specific embodiment of the invention provides；
Fig. 2 is the flow diagram for the picture quality assessment method that the specific embodiment of the invention provides；
Fig. 3 is the sub-process schematic diagram for the picture quality assessment method that the specific embodiment of the invention provides；
Fig. 4 is the sub-process schematic diagram for the picture quality assessment method that the specific embodiment of the invention provides；
Fig. 5 is the sub-process schematic diagram for the picture quality assessment method that the specific embodiment of the invention provides；
Fig. 6 is the schematic block diagram for the picture quality evaluation device that the specific embodiment of the invention provides；
Fig. 7 is the schematic block diagram for the picture quality evaluation device that another specific embodiment of the present invention provides；
Fig. 8 is the schematic frame of the model acquiring unit for the picture quality evaluation device that the specific embodiment of the invention provides
Figure；
Fig. 9 is the schematic frame of the clarity analytical unit for the picture quality evaluation device that the specific embodiment of the invention provides
Figure；
Figure 10 is the schematic frame of the deep learning unit for the picture quality evaluation device that the specific embodiment of the invention provides
Figure；
Figure 11 is a kind of schematic block diagram for computer equipment that the specific embodiment of the invention provides.
Specific embodiment
In order to more fully understand technology contents of the invention, combined with specific embodiments below to technical solution of the present invention into
One step introduction and explanation, but not limited to this.
It should be appreciated that ought use in this specification and in the appended claims, term " includes " and "comprising" instruction
Described feature, entirety, step, operation, the presence of element and/or component, but one or more of the other feature, whole is not precluded
Body, step, operation, the presence or addition of element, component and/or its set.
It is also understood that mesh of the term used in this present specification merely for the sake of description specific embodiment
And be not intended to limit the application.As present specification and it is used in the attached claims, unless on
Other situations are hereafter clearly indicated, otherwise " one " of singular, "one" and "the" are intended to include plural form.
It will be further appreciated that the term "and/or" used in present specification and the appended claims is
Refer to any combination and all possible combinations of one or more of associated item listed, and including these combinations.
Fig. 1 and Fig. 2 are please referred to, Fig. 1 is that the application scenarios of picture quality assessment method provided in an embodiment of the present invention are illustrated
Figure.Fig. 2 is the schematic flow chart of picture quality assessment method provided in an embodiment of the present invention.The picture quality assessment method can
Applied in server 20, exist in the form of picture quality evaluates platform, which can be counted with user terminal 10
According to interaction.Wherein, the user of user terminal 10 is usually a certain user using platform, such as the user of payment platform,
Facial image can be sent to server 20 by the judgement APP of user terminal 10, the face figure based on the received of server 20
As and to terminal feeds back corresponding evaluation result.
In addition, above-mentioned picture quality assessment method can also be applied to mobile terminal, with the shape of picture quality evaluation APP
Formula exists, and after user evaluates APP acquisition facial image by picture quality, is lived inside mobile terminal according to facial image
Physical examination is surveyed, and shows corresponding evaluation result.
As shown in Fig. 2, the picture quality assessment method of the embodiment of the present invention comprising steps of
S110, several training images are obtained.
In the present embodiment, above-mentioned training image is referred to for training to obtain the data of deep learning model, one
As using camera obtain image.
S120, quality score is carried out to training image and is marked.
In the present embodiment, specifically the face deflection angle in training image, human face light, facial size are beaten
Point, it is divided into five classes: Bad [0,20], Poor [20,40], Fair [40,60], Good in the section 0-100 according to quality when marking
[60,80], Excellent [80,100].First give a mark in the section 0-100 according to corresponding five grades to face deflection angle,
It gives a mark in the section 0-100 according to corresponding five grades to human face light, to facial size in the section 0-100 according to corresponding five
A grade marking, finally calculate according to marking the quality score and mark of training image, which is also classified into Bad
[0,20], Poor [20,40], Fair [40,60], Good [60,80], five classifications of Excellent [80,100].
It gives a mark to training image from illumination, size and deflection angle, this three aspect is also included in image evaluation process
In, it realizes the accuracy for improving picture quality judgement, greatly improves the accuracy rate of true environment human face identification.
S130, the training image after mark is trained, to obtain deep learning model.
In the present embodiment, deep learning model refer to using training image training CNN (convolutional neural networks,
Convolutional Neural Network) sorter network, a flight data recorder is obtained with this.
In one embodiment, as shown in figure 3, above-mentioned step S130 may include S131~S135.
S131, training of judgement image size whether meet preset requirement.
In the present embodiment, preset requirement refers to that the size of image is 224mm*224mm.
S132, if it is not, then carrying out size processing to training image, and S133 is entered step, if so, entering step
S133。
The size of training image is not 224mm*224mm, it is necessary to be processed to input picture, that is, from training image
In take the training image blocks of 224mm*224mm at random as the training image for input.
S133, several training image blocks are screened from training image.
In the present embodiment, training image blocks refer to that, when the size of training image meets preset requirement, training image is
It after then being taken to training image, is formed several for training image blocks when the size of training image is unsatisfactory for preset requirement
Image block, several image blocks i.e. training image blocks screened.
In order to guarantee trained performance, the training of multiple 224mm*224mm is selected at random from every training image at random
Image block, in the present embodiment, setting select 10.
S134, the corresponding feature vector of training image blocks is extracted, and establishes training set.
Specifically, it is carried out using CNN (convolutional neural networks, Convolutional Neural Network) sorter network
The model that deep learning training obtains can automatically extract feature.
In the present embodiment, feature vector refer to a certain class object be different from other class objects corresponding (essence) feature or
The set of characteristic or these features and characteristic.It is characterized in by measuring or handling the data that can be extracted.For image
Speech, every piece image all have the unique characteristics that can be different from other class images, some are oneself that can be perceive intuitively that
Right feature, such as brightness, edge, texture and color；Some are then that needs are just getable by transformation or processing, such as square, directly
Side's figure and main composition etc..The multiple or multifrequency nature of a certain class object is combined, a feature vector is formed and carrys out generation
The table class object, if only single number feature, feature vector is an one-dimensional vector, if it is the combination of n characteristic,
It is then a n dimensional feature vector.Input of such feature vector often as identifying system.In fact, a n dimensional feature is exactly
Point in n-dimensional space, and identify that the task of classification is exactly a kind of division found to this n-dimensional space.
The feature vector of each training image blocks is obtained, extracts maximum, minimum and the average value group of 10 randomized blocks respectively
A new vector f eature pooling (min, average, max) is synthesized, i.e., (maximum value, average value are minimum for feature pool
Value), all feature pools are combined into a vector, for indicating full figure.
One feature vector is corresponding with for the quality comprehensive score of each training image, is established with this feature vector
Training set can be used as the feature vector of a standard, for training CNN sorter network.
S135, CNN sorter network is trained using training set and training image, to obtain deep learning model.
In the present embodiment, CNN sorter network refers to the network that image classification is carried out using convolutional neural networks.
Above-mentioned Bad [0,20], Poor [20,40], Fair [40,60], Good [60,80], Excellent [80,100]
Training image in five classifications has the feature vector of standard.
When training CNN sorter network, training image is input to CNN sorter network, this training image is classified by CNN
Each one group of vector of layer process of convolution output quantity of network, but for completely by CCN sorter network that stochastic filtering device constructs and
Speech, output think the training image equiprobability be the corresponding a certain standard of training image of above-mentioned five classifications feature to
Amount, then can define a loss function, such as common MSE (mean square error, mean squared error), it is assumed that L at this time
L output valve is constantly fed back into entire CCN when training set being inputted CNN sorter network every time for the output of the loss function
Sorter network, so that penalty values L does small, that is, allows each convolutional layer to modify the weight of the filter of each layer convolution
Filter can combine the specific mode of detection of optimization, so as to form deep learning model, so that each training
Image by CCN sorter network extract feature vector and its corresponding to standard feature vector gap meet set want
It asks.
Training CNN sorter network is specifically the filter of each convolutional layer in training CNN sorter network, these is allowed to filter
Wave device group has high activation to specific mode, to reach the classification purpose of CNN sorter network.
Feature is extracted with trained CNN sorter network, CNN deep learning network automatically extracts feature, also
It is to say that deep learning is equivalent to a flight data recorder, sample is thrown into, is obtained in output as a result, obtaining the spy for belonging to the sample
Vector is levied, intermediate process is automatically performed by deep learning.
S140, image is obtained.
In the present embodiment, the facial image for needing to determine from the evaluation APP input of the picture quality of user terminal 10, with this
Image to be determined as input carries out scoring judgement to the image.
S150, clarity analysis is carried out to image, to obtain image definition score.
In the quality evaluation of non-reference picture, the clarity of image is to measure the important indicator of picture quality superiority and inferiority, it
Can be preferably corresponding with the subjective feeling of people, the clarity of image is not high to show the fuzzy of image.
In one embodiment, as shown in figure 4, above-mentioned steps S150 may include having S151~S152.
S151, energy gradient sharpness computation is carried out to image, to obtain calculated result；
S152, the normalization calculated result, to obtain image definition score.
In the present embodiment, above-mentioned image definition score refers to that the clarity according to image is given a mark, and is formed
Score.Specifically, Energy energy gradient definition judgment carried out to the image of input, Energy gradient function such as:Here D (f) is to be based on
The face picture score that Energy power gradient obtains, x and y are the pixel coordinate of image respectively.According to calculated result, and will
Obtained gradient energy value normalizes within the scope of 1-100.Energy gradient function is more suitable for Real-Time Evaluation image definition.
Certainly, in other embodiments, can also using entropy function, Brenner gradient function, Tenengrad gradient function,
Laplacian gradient function, SMD (Sum Modul us Difference, gray variance) function, (gray variance multiplies SMD2
Product) modes such as function and variance function calculate image definition score.
In conjunction with the analysis of image definition, the accuracy of picture quality judgement can be improved, greatly improved under true environment
The accuracy rate of recognition of face.
S160, deep learning is carried out to image using deep learning model, to obtain image quality score.
In one embodiment, as shown in figure 5, above-mentioned steps S160 may include S161~S164.
S161, several testing image blocks are extracted to image.
In the present embodiment, testing image block is taken at random having a size of 224mm*224mm's by the image progress inputted
Image block.Image is extracted to the testing image block of several 224mm*224mm sizes at random；Can set herein selection 10 to
Altimetric image block can wait other numbers in the number of other embodiments, the testing image block for 15.
S162, several testing image blocks are inputted into deep learning model, to extract the feature of several testing image blocks
Vector.
10 testing image blocks are sequentially placed into the deep learning model that training obtains, utilize deep learning model extraction
The feature vector of several testing image blocks out.
S163, the mass fraction for predict to the feature vector of several testing image blocks several testing image blocks.
Specifically, feature vector CNN sorter network extracted uses the mass fraction carried out with SVM prediction mode pre-
It surveys, the score that prediction is obtained is as the quality score of face picture.
The full articulamentum of CNN sorter network exports 256 dimensional feature vectors；SVM calls directly the open source library libsvm, uses
RBF core, it is that scikit-learn is arranged automatically that C, which takes 0.9, Gamma,.256 dimensional feature vectors that CNN sorter network is exported as
Obtained mass fraction is normalized to 0-100 by the input feature value of SVM.
The open source library libsvm is called to carry out score calculating, the statement of all functions and structural body definition of libSVM SVM
It is all contained in libSVM.h file, the meaning of some structural bodies is as follows in libSVM.h:
struct svm_node
{
int index；
double value；
}。
The structural body defines one " SVM node ", it may be assumed that index i and its corresponding ith feature value.N in this way
The SVM node of the same category number just constitutes a SVM input vector.That is: a SVM input vector can be expressed as
Form:
Class label index 1: the index of characteristic value 12: the index of characteristic value 23: characteristic value 3...
Input vector as several is input to libSVM to be trained, alternatively, one class label of input is unknown
Vector it is predicted, 0-100 is normalized in prediction result, the mass fraction of testing image block is obtained with this.
S164, obtain several testing image blocks mass fraction average value, to obtain image quality score.
It is averaged by predicting the mass fraction of each testing image block, then by the mass fraction of these testing image blocks
It is worth the mass fraction as testing image picture.
S170, synthetic image articulation score and image quality score form evaluation result.
Specifically, image definition score and image quality score are weighted summation according to specified weight, to be formed
Evaluation result.
In the present embodiment, the image definition score and be based on deep learning that integration capability gradient sharpness computation obtains
The image quality score that model and SVM are predicted provides the evaluation result of image, calculates according to the following equation:
Score=ScoreCNN × 0.6+D (f) × 0.4, wherein score is the comprehensive score of image, i.e. evaluation result,
ScoreCNN is the image quality score predicted based on deep learning model and SVM, and D (f) refers to image definition point
Number.
S180, output evaluation result.
The evaluation result that will acquire feeds back to the picture quality evaluation APP of user terminal 10, so that user knows input
Image score.
Above-mentioned picture quality assessment method, by first being scored from illumination, size and deflection angle training image,
Training image is subjected to quality score, utilizes the training set for the feature vector composition that training image extracts, the quality of training image
Score is trained adjustment to CNN sorter network, to obtain deep learning mode, in use, image is carried out clarity scoring
And deep learning model scores, then is weighted synthesis to two scores, so that evaluation result is obtained, to improve figure
The accuracy that tablet quality determines greatly improves the accuracy rate of true environment human face identification.
Referring to Fig. 6, Fig. 6 is the schematic block diagram for the picture quality evaluation device 200 that the specific embodiment of the invention provides；
As shown in fig. 6, the picture quality evaluation device 200 includes:
Image acquisition unit 204, for obtaining image.
Clarity analytical unit 205, for carrying out clarity analysis to image, to obtain image definition score.
Deep learning unit 206, for using deep learning model to image carry out deep learning, and to learning outcome into
Row Score on Prediction, to obtain image quality score.
Comprehensive unit 207 is used for synthetic image articulation score and image quality score, forms evaluation result.
As a result output unit 208, for exporting evaluation result.
In one embodiment, as shown in fig. 7, above-mentioned device further include:
Training image acquiring unit 201, for obtaining several training images；
Unit 202 is marked, for carrying out quality score to training image and marking；
Model acquiring unit 203, for being trained to the training image after mark, to obtain deep learning model.
In one embodiment, as shown in figure 8, above-mentioned model acquiring unit 203 includes:
Whether size judgment sub-unit 2031, the size for training of judgement image meet preset requirement.
Size handles subelement 2032, is used for if it is not, then carrying out size processing to training image.
Subelement 2033 is screened, for screening several training image blocks from training image.
Training set establishes subelement 2034, for extracting the corresponding feature vector of training image blocks, and establishes training set.
Network training subelement 2035, for training CNN sorter network using training set and training image, to obtain depth
Learning model.
In one embodiment, as shown in figure 9, above-mentioned clarity analytical unit 205 includes:
Sharpness computation subelement 2051, for carrying out energy gradient sharpness computation to image, to obtain calculated result.
Normalizing subelement 2052, for normalizing the calculated result, to obtain image definition score.
In one embodiment, as shown in Figure 10, above-mentioned deep learning unit 206 includes:
Image block extracts subelement 2061, for extracting several testing image blocks to image.
Subelement 2062 is inputted, for several testing image blocks to be inputted deep learning model, is waited for extracting several
The feature vector of altimetric image block.
It predicts subelement 2063, carries out predicting several testing images for the feature vector to several testing image blocks
The mass fraction of block.
Average value obtains subelement 2064, the average value of the mass fraction for obtaining several testing image blocks, to obtain
To image quality score.
It should be noted that it is apparent to those skilled in the art that, above-mentioned picture quality evaluation device
200 and each unit specific implementation process, can with reference to the corresponding description in preceding method embodiment, for convenience of description and
Succinctly, details are not described herein.
Above-mentioned picture quality evaluation device 200 can be implemented as a kind of form of computer program, which can
To be run in computer equipment as shown in figure 11.
Figure 11 is please referred to, Figure 11 is a kind of schematic block diagram of computer equipment provided by the embodiments of the present application.The calculating
Machine equipment 500 can be terminal, be also possible to server, wherein terminal can be smart phone, tablet computer, notebook electricity
Brain, desktop computer, personal digital assistant and wearable device etc. have the electronic equipment of communication function.Server can be independence
Server, be also possible to the server cluster of multiple servers composition.
Refering to fig. 11, which includes processor 502, memory and the net connected by system bus 501
Network interface 505, wherein memory may include non-volatile memory medium 503 and built-in storage 504.
The non-volatile memory medium 503 can storage program area 5031 and computer program 5032.The computer program
5032 include program instruction, which is performed, and processor 502 may make to execute a kind of picture quality assessment method.
The processor 502 is for providing calculating and control ability, to support the operation of entire computer equipment 500.
The built-in storage 504 provides environment for the operation of the computer program 5032 in non-volatile memory medium 503, should
When computer program 5032 is executed by processor 502, processor 502 may make to execute a kind of picture quality assessment method.
The network interface 505 is used to carry out network communication with other equipment.It will be understood by those skilled in the art that in Figure 11
The structure shown, only the block diagram of part-structure relevant to application scheme, does not constitute and is applied to application scheme
The restriction of computer equipment 500 thereon, specific computer equipment 500 may include more more or fewer than as shown in the figure
Component perhaps combines certain components or with different component layouts.
Wherein, the processor 502 is for running computer program 5032 stored in memory, to realize following step
It is rapid:
Obtain image；
Clarity analysis is carried out to image, to obtain image definition score；
Deep learning is carried out to image using deep learning model, and Score on Prediction is carried out to learning outcome, to obtain figure
As mass fraction；
Synthetic image articulation score and image quality score form evaluation result；
Export evaluation result.
In one embodiment, processor 502 carries out deep learning to image in the realization use deep learning model, with
Before obtaining image quality score step, following steps are also realized:
Obtain several training images；
Quality score is carried out to training image and is marked；
Training image after mark is trained, to obtain deep learning model.
In one embodiment, training image of the processor 502 after realizing described pair of mark is trained, to obtain depth
When learning model step, it is implemented as follows step:
Whether the size of training of judgement image meets preset requirement；
If it is not, then carrying out size processing to training image, and enter next step, if so, into next step；
Several training image blocks are screened from training image；
The corresponding feature vector of training image blocks is extracted, and establishes training set；
Using training set and training image training CNN sorter network, to obtain deep learning model.
In one embodiment, processor 502 is described to image progress clarity analysis in realization, to obtain image definition
When score step, it is implemented as follows step:
Energy gradient sharpness computation is carried out to image, to obtain calculated result；
The calculated result is normalized, to obtain image definition score.
In one embodiment, processor 502 carries out deep learning to image in the realization use deep learning model, and
Score on Prediction is carried out to learning outcome and is implemented as follows step when obtaining image quality score step:
Several testing image blocks are extracted to image；
Several testing image blocks are inputted into deep learning model, to extract the feature vector of several testing image blocks；
Predict to the feature vector of several testing image blocks the mass fraction of several testing image blocks；
The average value of the mass fraction of several testing image blocks is obtained, to obtain image quality score.
In one embodiment, processor 502 is realizing the synthetic image articulation score and image quality score, shape
When at evaluation result step, it is implemented as follows step:
Image definition score and image quality score are weighted summation according to specified weight, to form evaluation knot
Fruit.
It should be appreciated that in the embodiment of the present application, processor 502 can be central processing unit (Central
Processing Unit, CPU), which can also be other general processors, digital signal processor (Digital
Signal Processor, DSP), specific integrated circuit (Application Specific Integrated Circuit,
ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic
Device, discrete gate or transistor logic, discrete hardware components etc..Wherein, general processor can be microprocessor or
Person's processor is also possible to any conventional processor etc..
Those of ordinary skill in the art will appreciate that be realize above-described embodiment method in all or part of the process,
It is that relevant hardware can be instructed to complete by computer program.The computer program includes program instruction, computer journey
Sequence can be stored in a storage medium, which is computer readable storage medium.The program instruction is by the department of computer science
At least one processor in system executes, to realize the process step of the embodiment of the above method.
Therefore, the present invention also provides a kind of storage mediums.The storage medium can be computer readable storage medium.This is deposited
Storage media is stored with computer program, and wherein computer program includes program instruction.The program instruction makes when being executed by processor
Processor executes following steps:
Obtain image；
Clarity analysis is carried out to image, to obtain image definition score；
Deep learning is carried out to image using deep learning model, and Score on Prediction is carried out to learning outcome, to obtain figure
As mass fraction；
Synthetic image articulation score and image quality score form evaluation result；
Export evaluation result.
In one embodiment, the processor is realized described using deep learning model pair in the instruction of execution described program
Image carries out deep learning, before obtaining image quality score step, also realizes following steps:
Obtain several training images；
Quality score is carried out to training image and is marked；
Training image after mark is trained, to obtain deep learning model.
In one embodiment, training image of the processor after described pair of mark is realized in the instruction of execution described program
It is trained, when obtaining deep learning model step, is implemented as follows step:
Whether the size of training of judgement image meets preset requirement；
If it is not, then carrying out size processing to training image, and enter next step, if so, into next step；
Several training image blocks are screened from training image；
The corresponding feature vector of training image blocks is extracted, and establishes training set；
Using training set and training image training CNN sorter network, to obtain deep learning model.
In one embodiment, the processor is realized described to image progress clarity point in the instruction of execution described program
Analysis, when obtaining image definition score step, is implemented as follows step:
Energy gradient sharpness computation is carried out to image, to obtain calculated result；
The calculated result is normalized, to obtain image definition score.
In one embodiment, the processor is realized described using deep learning model pair in the instruction of execution described program
Image carries out deep learning, and carries out Score on Prediction to learning outcome, and when obtaining image quality score step, specific implementation is such as
Lower step:
Several testing image blocks are extracted to image；
Several testing image blocks are inputted into deep learning model, to extract the feature vector of several testing image blocks；
Predict to the feature vector of several testing image blocks the mass fraction of several testing image blocks；
The average value of the mass fraction of several testing image blocks is obtained, to obtain image quality score.
In one embodiment, the processor realizes the synthetic image articulation score executing described program instruction
And image quality score is implemented as follows step when forming evaluation result step:
Image definition score and image quality score are weighted summation according to specified weight, to form evaluation knot
Fruit.
The storage medium can be USB flash disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), magnetic disk
Or the various computer readable storage mediums that can store program code such as CD.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware
With the interchangeability of software, each exemplary composition and step are generally described according to function in the above description.This
A little functions are implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Specially
Industry technical staff can use different methods to achieve the described function each specific application, but this realization is not
It is considered as beyond the scope of this invention.
In several embodiments provided by the present invention, it should be understood that disclosed device and method can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary.For example, the division of each unit, only
Only a kind of logical function partition, there may be another division manner in actual implementation.Such as multiple units or components can be tied
Another system is closed or is desirably integrated into, or some features can be ignored or not executed.
The steps in the embodiment of the present invention can be sequentially adjusted, merged and deleted according to actual needs.This hair
Unit in bright embodiment device can be combined, divided and deleted according to actual needs.In addition, in each implementation of the present invention
Each functional unit in example can integrate in one processing unit, is also possible to each unit and physically exists alone, can also be with
It is that two or more units are integrated in one unit.
If the integrated unit is realized in the form of SFU software functional unit and when sold or used as an independent product,
It can store in one storage medium.Based on this understanding, technical solution of the present invention is substantially in other words to existing skill
The all or part of part or the technical solution that art contributes can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, terminal or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.
It is above-mentioned that technology contents of the invention are only further illustrated with embodiment, in order to which reader is easier to understand, but not
It represents embodiments of the present invention and is only limitted to this, any technology done according to the present invention extends or recreation, by of the invention
Protection.Protection scope of the present invention is subject to claims.
Claims (10)
1. picture quality assessment method characterized by comprising
Obtain image；
Clarity analysis is carried out to image, to obtain image definition score；
Deep learning is carried out to image using deep learning model, and Score on Prediction is carried out to learning outcome, to obtain image matter
Measure score；
Synthetic image articulation score and image quality score form evaluation result；
Export evaluation result.
2. picture quality assessment method according to claim 1, which is characterized in that described to use deep learning model to figure
As carrying out deep learning, before obtaining image quality score, further includes:
Obtain several training images；
Quality score is carried out to training image and is marked；
Training image after mark is trained, to obtain deep learning model.
3. picture quality assessment method according to claim 2, which is characterized in that described pair mark after training image into
Row training, to obtain deep learning model, comprising:
Whether the size of training of judgement image meets preset requirement；
If it is not, then carrying out size processing to training image, and enter next step, if so, into next step；
Several training image blocks are screened from training image；
The corresponding feature vector of training image blocks is extracted, and establishes training set；
Using training set and training image training CNN sorter network, to obtain deep learning model.
4. picture quality assessment method according to claim 3, which is characterized in that described to carry out clarity point to image
Analysis, to obtain image definition score, comprising:
Energy gradient sharpness computation is carried out to image, to obtain calculated result；
The calculated result is normalized, to obtain image definition score.
5. picture quality assessment method according to claim 4, which is characterized in that described to use deep learning model to figure
Score on Prediction is carried out as carrying out deep learning, and to learning outcome, to obtain image quality score, comprising:
Several testing image blocks are extracted to image；
Several testing image blocks are inputted into deep learning model, to extract the feature vector of several testing image blocks；
Predict to the feature vector of several testing image blocks the mass fraction of several testing image blocks；
The average value of the mass fraction of several testing image blocks is obtained, to obtain image quality score.
6. picture quality assessment method according to any one of claims 1 to 5, which is characterized in that the synthetic image is clear
Clear degree score and image quality score form evaluation result, comprising:
Image definition score and image quality score are weighted summation according to specified weight, to form evaluation result.
7. picture quality evaluation device characterized by comprising
Image acquisition unit, for obtaining image；
Clarity analytical unit, for carrying out clarity analysis to image, to obtain image definition score；
Deep learning unit for carrying out deep learning to image using deep learning model, and carries out score to learning outcome
Prediction, to obtain image quality score；
Comprehensive unit is used for synthetic image articulation score and image quality score, forms evaluation result；
As a result output unit, for exporting evaluation result.
8. picture quality evaluation device according to claim 7, which is characterized in that the clarity analytical unit includes:
Sharpness computation subelement, for carrying out energy gradient sharpness computation to image, to obtain calculated result；
Normalizing subelement, for normalizing the calculated result, to obtain image definition score.
9. a kind of computer equipment, which is characterized in that including memory, processor and be stored on the memory and can be in institute
The computer program run on processor is stated, the processor is realized when executing the computer program as in claim 1 to 6
Picture quality assessment method described in any one.
10. a kind of storage medium, which is characterized in that the storage medium is stored with computer program, the computer program packet
Program instruction is included, described program instruction makes the processor execute such as claim 1 to 6 any one when being executed by a processor
The picture quality assessment method.
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Application publication date: 20190104 |