CN107371015A - One kind is without with reference to contrast modified-image quality evaluating method - Google Patents

One kind is without with reference to contrast modified-image quality evaluating method Download PDF

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Publication number
CN107371015A
CN107371015A CN201710600600.5A CN201710600600A CN107371015A CN 107371015 A CN107371015 A CN 107371015A CN 201710600600 A CN201710600600 A CN 201710600600A CN 107371015 A CN107371015 A CN 107371015A
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image
contrast
modified
contrast modified
evaluating method
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曾焕强
邢露
陈婧
朱建清
蔡灿辉
马凯光
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Huaqiao University
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Huaqiao University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N17/00—Diagnosis, testing or measuring for television systems or their details
    • H04N17/004—Diagnosis, testing or measuring for television systems or their details for digital television systems

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
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  • General Health & Medical Sciences (AREA)
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Abstract

The present invention relates to one kind without contrast modified-image quality evaluating method is referred to, subjective perception characteristic of the human visual system for contrast modified-image is taken into full account, extracts four characteristics of image respectively：Definition, comentropy, brightness and contrast；And combination supporting vector machine SVM (Support Vector Machine) is trained to obtain the mapping relations MODEL C CQAM of the characteristic vector of contrast modified-image and subjective quality scores to extracted characteristics of image；The mass fraction of contrast modified-image is finally evaluated using the mapping relations MODEL C CQAM of training gained.Method of the present invention calculates simply, and practicality is stronger, and close with human eye subjective quality assessment, can evaluate the mass fraction of contrast modified-image well.

Description

One kind is without with reference to contrast modified-image quality evaluating method
Technical field
The present invention relates to image processing field, is commented more specifically to one kind without contrast modified-image quality is referred to Valency method.
Background technology
With the fast development of cloud computing, big data, multimedia communication and consumer electronics, image quality evaluation turns into science Boundary and the important research content of industrial quarters.Because image is readily incorporated various noises, example when obtaining, encode, transmitting and showing Such as Gaussian noise, fuzzy distortion, compression artefacts, color distortion, contrast change etc., how accurate evaluation picture quality is to promoting Image processing techniques development has important theory significance and actual application value.
Image quality evaluating method is broadly divided into subjective quality assessment and evaluating objective quality.
Subjective picture quality evaluation is that final score is judged image and provided taking human as observer.It is but subjective Image quality evaluation is not often suitable for practical application because it is time-consuming, laborious and is easily influenceed by factors such as environment.
Objective image quality evaluation imitates human visual system with evaluation result and subjectivity by founding mathematical models Quality evaluation is consistent, and simple to operate, with more actual application value.
Objective image quality evaluation method according to reference picture provide degree can be divided into full reference image quality appraisement method, Half reference image quality appraisement method and non-reference picture quality appraisement method.
Full reference image quality appraisement method utilizes the full detail of reference picture, by calculating reference picture and distortion map The similarity of picture, obtain the quality evaluation value of distorted image.Such as classical mean square error (Mean Square Error, MSE) With Y-PSNR (Peak Signal to Noise Ratio, PSNR).
Half reference image quality appraisement only uses the perceived quality that reference picture Partial Feature information carrys out calculated distortion image. But in actual applications often without reference to image information, both approaches have certain limitation.Non-reference picture matter Amount evaluation method can make corresponding evaluation score without reference to image to image, have higher practical value.
Typical non-reference picture quality appraisement method, there is the non-reference picture quality appraisement model based on two-stage framework (Blind Image Quality Index, BIQI), the non-reference picture matter based on spatial domain normalization coefficient extraction regression model Evaluation model (Blind/Referenceless Image Spatial Quality Evaluator, BRISQUE) etc. is measured, all Most of type of distortion (e.g., compression, JPEG distortions) can be assessed well.
However, existing many image quality evaluating methods are all unable to distortion caused by the change of effective evaluation contrast.At present Lack the non-reference picture quality appraisement method to deteriroation of image quality caused by contrast change in image processing field.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of practical and easy calculating without reference Contrast modified-image quality evaluating method.
Technical scheme is as follows：
For one kind without contrast modified-image quality evaluating method is referred to, step is as follows：
1) the contrast modified-image I for training pattern is inputtedk, k=1,2 ..., K, K be training process in contrast Modified-image sum；
2) to contrast modified-image IkPre-processed to obtain pretreatment image I'k；
Calculate contrast modified-image IkCharacteristics of image：Contrast modified-image IkDefinition and comentropy, and in advance Handle image I 'kBrightness and contrast, the characteristic vector using these characteristics of image as current contrast modified-image；
3) corresponding subjective quality scores are combined to the characteristic vector of all contrast modified-images using SVMs It is trained, obtains the characteristic vector of contrast modified-image and the mapping relations model of subjective quality scores；
4) input contrast modified-image to be measured, according to the method for step 2) be calculated the feature of image to be tested to Amount；
5) characteristic vector of contrast modified-image to be measured is inputted into mapping relations model, exports contrast variation diagram to be measured The mass fraction of picture.
Preferably, to contrast modified-image IkCarrying out pretreatment includes gray processing processing, filtering process.
Preferably, to contrast modified-image IkThe step of being pre-processed is as follows：
2.1) gray processing contrast modified-image Ik, gray level image is obtained, only retains monochrome information；
2.2) gray level image is filtered, step is as follows：
2.2.1 Fourier transformation) is carried out to gray level image, obtains frequency domain figure as Fk；
2.2.2) utilize Contrast sensitivity functionTo frequency domain figure as FkCarry out Filtering process, obtain filtered image
2.2.3) to filtered imageCarry out Fourier inversion and obtain pretreatment image I 'k；
Wherein,U and v be corresponding pixel points frequency domain variable, k=1,2 ..., K.
Preferably, in step 2), definition
Wherein,Represent respectively on x and y directions Difference, M and N represent contrast modified-image I respectivelykSize in the horizontal and vertical directions.
Preferably, in step 2), comentropy
Wherein,Represent contrast modified-image IkMiddle gray value is the ratio shared by i pixel, and f (i) is Contrast modified-image IkMiddle gray value is the frequency that i occurs, and (0≤i≤255), M and N represent image horizontal and vertical respectively The upward size of Nogata.
Preferably, in step 2), monochrome information
Preferably, in step 2), contrast information
Wherein, P and Q represents pretreatment image I ' respectivelykSize in the horizontal and vertical directions.
Preferably, all contrast modified-image I will be based onkThe eigenvalue cluster of gained is calculated into eigenvectors matrixUsing SVMs to all contrast modified-image IkEigenvectors matrix R combine it is subjective Mass fraction is trained, and obtains the characteristic vector of contrast modified-image and the mapping relations model of subjective quality scores.
Preferably, the training process of mapping relations model is off-line training.
Beneficial effects of the present invention are as follows：
It is of the present invention without refer to contrast modified-image quality evaluating method, take into full account human visual system for The subjective perception characteristic of contrast modified-image, four characteristics of image are extracted respectively：Definition, comentropy, brightness and contrast； And combination supporting vector machine SVM (Support Vector Machine) is trained to obtain contrast to extracted characteristics of image The characteristic vector of modified-image and the mapping relations MODEL C CQAM of subjective quality scores；Finally closed using the mapping of training gained It is the mass fraction of MODEL C CQAM evaluation contrast modified-images.
Method of the present invention calculates simply, and practicality is stronger, and close with human eye subjective quality assessment, can be well Evaluate the mass fraction of contrast modified-image.
Brief description of the drawings
Fig. 1 is the FB(flow block) of training step of the present invention；
Fig. 2 is the FB(flow block) of evaluation procedure of the present invention.
Embodiment
The present invention is described in further detail below in conjunction with drawings and Examples.
The present invention in order to solve not being suitable for existing for prior art contrast change caused by image quality distortion evaluation Phenomenon, make up deficiency of the algorithm to image quality distortion evaluation caused by contrast change of prior art, there is provided one kind is without ginseng Contrast modified-image quality evaluating method is examined, as shown in Figure 1 and Figure 2, method of the present invention includes training step and evaluation Step, wherein, step 1), step 2), step 3) they are training step, and the training process of mapping relations model is off-line training；Step It is rapid 4), step 5) be evaluation procedure.
Comprise the following steps that：
1) the contrast modified-image I for training pattern is inputtedk, k=1,2 ..., K, K be training process in contrast Modified-image sum.
2) to contrast modified-image IkPre-processed to obtain pretreatment image I'k, calculate contrast modified-image Ik's Characteristics of image：Contrast modified-image IkDefinition and comentropy, and pretreatment image I'kBrightness and contrast, by this A little characteristic vectors of the characteristics of image as current contrast modified-image.
In the present embodiment, to contrast modified-image IkCarrying out pretreatment includes gray processing processing, filtering process.
To contrast modified-image IkThe step of being pre-processed is as follows：
2.1) gray processing contrast modified-image Ik, gray level image is obtained, only retains monochrome information；
2.2) using Contrast sensitivity function (Contrast Sensitivity Filtering, CSF) to gray processing figure As being filtered, step is as follows：
2.2.1 Fourier transformation) is carried out to gray level image, obtains frequency domain figure as Fk；
2.2.2) utilize Contrast sensitivity functionTo frequency domain figure as FkCarry out Filtering process, obtain filtered image
2.2.3) to filtered imageCarry out Fourier inversion and obtain pretreatment image I 'k；
Wherein,U and v is the frequency domain variable of corresponding pixel points, k=1,2 ..., K, K.
In the present embodiment, definition
Wherein,Represent respectively on x and y directions Difference, M and N represent contrast modified-image I respectivelykSize in the horizontal and vertical directions.
Comentropy
Wherein,Represent contrast modified-image IkMiddle gray value is the ratio shared by i pixel, and f (i) is Contrast modified-image IkMiddle gray value is the frequency that i occurs, and (0≤i≤255), M and N represent image horizontal and vertical respectively The upward size of Nogata.
Monochrome information
Contrast information
Wherein, P and Q represents pretreatment image I ' respectivelykSize in the horizontal and vertical directions.
3) using support vector machines (Support Vector Machine) to the feature of all contrast modified-images Vector is trained with reference to subjective quality scores, will be based on all contrast modified-image IkCalculate the eigenvalue cluster Cheng Te of gained Levy vector matrixUsing SVMs to all contrast modified-image IkCharacteristic vector square Battle array R combination subjective quality scores are trained, and obtain the characteristic vector of contrast modified-image and the mapping of subjective quality scores Relational model CCQAM.
4) the contrast modified-image of quality evaluation is treated in input, and contrast to be measured, which is calculated, according to the method for step 2) becomes Change the characteristic vector of image；
5) characteristic vector of contrast modified-image to be measured is inputted into mapping relations model, exports contrast variation diagram to be measured The mass fraction of picture.
Above-described embodiment is intended merely to the explanation present invention, and is not used as limitation of the invention.As long as according to this hair Bright technical spirit, above-described embodiment is changed, modification etc. will all fall in the range of the claim of the present invention.

Claims (9)

  1. It is 1. a kind of without with reference to contrast modified-image quality evaluating method, it is characterised in that step is as follows：
    1) the contrast modified-image I for training pattern is inputtedk, k=1,2 ..., K, K be training process in contrast change Total number of images；
    2) to contrast modified-image IkPre-processed to obtain pretreatment image I'k；
    Calculate contrast modified-image IkCharacteristics of image：Contrast modified-image IkDefinition and comentropy, and pretreatment Image I 'kBrightness and contrast, the characteristic vector using these characteristics of image as current contrast modified-image；
    3) the characteristic vector combination subjective quality scores of all contrast modified-images are trained using SVMs, obtained To the characteristic vector of contrast modified-image and the mapping relations model of subjective quality scores；
    4) contrast modified-image to be measured is inputted, the feature of contrast modified-image to be measured is calculated according to the method for step 2) Vector；
    5) characteristic vector of contrast modified-image to be measured is inputted into mapping relations model, exports contrast modified-image to be measured Mass fraction.
  2. It is 2. according to claim 1 without with reference to contrast modified-image quality evaluating method, it is characterised in that step 2) In, to contrast modified-image IkCarrying out pretreatment includes gray processing processing, filtering process.
  3. It is 3. according to claim 2 without with reference to contrast modified-image quality evaluating method, it is characterised in that to contrast Modified-image IkThe step of being pre-processed is as follows：
    2.1) gray processing contrast modified-image Ik, gray level image is obtained, only retains monochrome information；
    2.2) gray level image is filtered, step is as follows：
    2.2.1 Fourier transformation) is carried out to gray level image, obtains frequency domain figure as Fk；
    2.2.2) utilize Contrast sensitivity functionTo frequency domain figure as FkIt is filtered Processing, obtains filtered image
    2.2.3) to filtered imageCarry out Fourier inversion and obtain pretreatment image I 'k；
    Wherein,U and v be corresponding pixel points frequency domain variable, k=1,2 ..., K.
  4. 4. existing without reference contrast modified-image quality evaluating method, its feature according to any one of claims 1 to 3 In, in step 2), definition
    Wherein,The difference on x and y directions is represented respectively Point, M and N represent contrast modified-image I respectivelykSize in the horizontal and vertical directions.
  5. 5. existing without reference contrast modified-image quality evaluating method, its feature according to any one of claims 1 to 3 In, in step 2), comentropy
    Wherein,Represent contrast modified-image IkMiddle gray value is the ratio shared by i pixel, and f (i) is contrast Modified-image IkMiddle gray value is the frequency that i occurs, and (0≤i≤255), M and N represent image both horizontally and vertically respectively On size.
  6. 6. existing without reference contrast modified-image quality evaluating method, its feature according to any one of claims 1 to 3 In, in step 2), monochrome information
    Wherein, P and Q represents pretreatment image I ' respectivelykSize in the horizontal and vertical directions.
  7. 7. existing without reference contrast modified-image quality evaluating method, its feature according to any one of claims 1 to 3 In, in step 2), contrast information
    Wherein, P and Q represents pretreatment image I ' respectivelykSize in the horizontal and vertical directions.
  8. It is 8. according to claim 1 without with reference to contrast modified-image quality evaluating method, it is characterised in that institute will be based on There is contrast modified-image IkThe eigenvalue cluster of gained is calculated into eigenvectors matrixUtilize support Vector machine is to all contrast modified-image IkEigenvectors matrix R combination subjective quality scores be trained, contrasted Spend the characteristic vector of modified-image and the mapping relations model of subjective quality scores.
  9. It is 9. according to claim 1 without with reference to contrast modified-image quality evaluating method, it is characterised in that mapping relations The training process of model is off-line training.
CN201710600600.5A 2017-07-21 2017-07-21 One kind is without with reference to contrast modified-image quality evaluating method Pending CN107371015A (en)

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