CN110473181A - Screen content image based on edge feature information without ginseng quality evaluating method - Google Patents
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Abstract
The present invention relates to a kind of screen content images based on edge feature information without ginseng quality evaluating method, including the following steps: is calculated by DOG algorithm and obtains the mapping of the image border SCIs；The edge map information that will acquire carries out local normalization, in the case where given SCIs, calculates normalization pixel using local average subtraction and the normalized method of division；The extraction of edge feature vector is carried out, extracts marginal information from part normalization edge graph using the fitting of L- square distribution estimation method；Device is returned using ABPNN, and quality is perceived into maps feature vectors to subjective quality score, L- square is distributed SCIs image n dimension its subjective quality assessment score of edge feature vector sum that estimation is extracted and is put into training in ABPNN network, in the training stage, regression model is established using edge feature vector and its corresponding subjective differences mean value Opinion Score (DMOS) value.
Description
Technical field
The invention belongs to image quality evaluation fields, are related to screen content image without ginseng quality evaluating method.
Background technique
In recent years, the rapid development of communications, promotes the extensive use of telecommunication, such as online news and wide
It accuses, cloud computing etc., to generate the screen content image (SCIs) largely generated by the screen of various terminal equipment.With natural field
Scape image is compared, and SCIs has quite apparent structural information and statistical nature, because SCI includes the content that computer generates,
Such as text, chart, map, mode or illustration (such as logo, bar code, two dimensional code), other icons.
According to the existence of reference image information, traditional Images of Natural Scenery quality evaluating method is generally divided into three types
Type, i.e., entirely with reference to (FR), reduction with reference to (RR) and blind/without with reference to (NR) scheme.It is complete to join natural image quality evaluation algorithm and get over
Subtract parameter natural image evaluation algorithms, using structural similarity index (SSIM), basic theories is human visual system (HVS)
Perceptual image quality effectively estimated to image spatial feature susceptibility.Without ginseng natural image quality evaluation without necessarily referring to letter
Breath is a kind of method for adapting to actual conditions, such as measurement based on natural scene statistics, support vector regression, kernel study, base
In the measurement of deep learning.
Since SCIs and natural image have visibly different structure and statistical property, the particularity of visual quality research
It is still a challenging problem.The image quality evaluation of SCIs is to solve how to assess from various electronic equipments
Into in our eyes deformation SCIs perceived quality the problem of.SCIs is constituted there are two types of method, it is another one is in receiving end
Kind is during image processing, such as to transmit in transmitting terminal and compression, SCIs will appear various distortions.By patent
Applicant retrieve discovery, no ginseng image quality evaluation on screen content image apply it is very few, therefore, this patent proposes that one kind has
Effect based on SCIs visual quality evaluation method, main contents include being able to maintain the stability of digital terminal, and guidance is based on
Coding, enhancing, the image of SCIs processing redirect the improvement and development of scheduling algorithm.
Marginal information is the fundamental of SCIs image-region high-frequency region, and therefore, marginal information can be used to reflect SCI
Distortion level, analyze SCI marginal information be solve SCI perceived quality forecasting problem a kind of feasible method.In addition,
AdaBoosting algorithm gets up multiple relatively weak classifiers combinations, is configured to a stronger regression machine, in algorithm essence
It improves a lot on degree.This patent is based on SCIs marginal information, passes through AdaBoosting algorithm, building prediction SCIs image
Without ginseng Environmental Evaluation Model, SCIs image quality evaluation is predicted.
Summary of the invention
This patent establish it is a kind of without with reference to measure come assess screen content image perceived quality (SCIs) method,
The purpose is to the SCIs edge features by extracting, and are carried out using marginal information of the Gauss model to SCI visual quality effective
The capture of human eye vision difference, and the mapping of two class multi-scale edges is calculated by neural network model, effectively SCI is tested in reflection
Distortion level, using AdaBooting reverse transmittance nerve network, training is carried out without ginseng Environmental Evaluation Model for SCIs image
The prediction of visual quality evaluation.Technical solution is as follows:
A kind of screen content image based on edge feature information without ginseng quality evaluating method, including the following steps:
The first step is calculated by DOG algorithm and obtains the mapping of the image border SCIs；
Second step, the edge map information that will acquire carries out local normalization, in the case where given SCIs, using part
Average subtraction and the normalized method of division calculate normalization pixel；
Third portion carries out the extraction of edge feature vector, normalizes edge from part using the fitting of L- square distribution estimation method
Marginal information is extracted in figure.
4th step returns device for quality perception maps feature vectors to subjective quality score using ABPNN, L- square is distributed
SCIs image n dimension its subjective quality assessment score of edge feature vector sum that estimation is extracted is put into training in ABPNN network, is instructing
Practice the stage, establishes regression model using edge feature vector and its corresponding subjective differences mean value Opinion Score (DMOS) value；Prediction
Stage will test the feature vector that SCIs is calculated and be input in the model of study, predicts SCIs image quality evaluation score.
Training stage in step 4, using following method:
Guarantee the quantity Q of Weak Classifier, and by the subjective differences mean value Opinion Score (DMOS) of the SCIs in training set
It is mapped to [0,1]；Using AdaBoosting algorithm, pass through the original quality evaluation score and prediction matter to i-th of Weak Classifier
Difference between amount evaluation score is integrated, and estimates its error of quality appraisement Γ and corresponding distribution；It will be single weak using convex function
The weight of classifier is converted into corresponding weight, advantageously ensures that the weight of lower Weak Classifier is bigger, lower weak typing
The weight of device is smaller；Final SCIs image quality evaluation score output is calculated in conjunction with prediction output phase by weight.
It is proposed by the present invention effectively without ginseng screen content image quality evaluating method.This method is by human eye vision edge mould
Type is combined with AdaBoosing BP neural network.Two kinds of multi-scale edges of SCIs image are calculated first with DOG operator
Mapping.Then, after locally normalization edge mapping, estimation is distributed using L- square and is joined to be fitted its coefficient of the image border SCIs figure
Number is used as edge feature.Finally, using AdaBoosting BP neural network, by the n dimension edge feature vector of said extracted with
Its corresponding image quality evaluation score is put into training in model, and the objective quality of quality perception Feature Mapping to SCIs are commented
Valence score carries out SCIs image to predict its quality evaluation score.This method competitiveness with higher in SCI database
Can, it is stronger with the consistency of human visual system.
Detailed description of the invention
Fig. 1 SCIs example images figure
The flow chart that designs a model of Fig. 2 SCI image quality evaluation
Fig. 3 ABPNN algorithm flow chart
Fig. 4 ABPNN structure chart
Specific embodiment
SCIs image is as shown in Figure 1, detailed process trains as shown in Fig. 2, being divided into and predicts two stages, in training rank
Section, if the brightness of image of SCIs is I (x, y).
Evaluation procedure is as follows:
Step 1: edge graph calculates
This step calculates edge mapping using the difference algorithm (DOG algorithm) of Gaussian function.During image perception,
Human visual system is highly sensitive to marginal information, and details is the abundant component part of SCIs.Therefore, human vision DOG model
The difference of edge mapping can effectively be handled.In formula, I indicates the brightness mapping of test,Indicate convolution algorithm, image
Edge graph E can be indicated are as follows:
Wherein GσIt is isotropism Gaussian function and scale parameter σ
DOG model is expressed as
DOG=Gσ1(x,y)-Gσ2(x,y)
DOG algorithm can be used to improve visibility and the profile information of image at edge etc. as a kind of enhancing algorithm
Details.By using different parameter (σ1σ2) and the two kinds of edges mappings of 3 × 3 Gauss square window can effectively capture
To edge degradation.
Step 2: part normalization
Normalization is the integral process based on statistical property, as contrast normalizes.The Shandong of model can be improved in normalization
Stick calculates normalization pixel using local average subtraction and the normalized method of division in the case where given SCI image,
Its expression formula is
P (x, y) andRespectively indicate the original value and normal value at position (x, y)；X ∈ 1,2,3 ..., X } and y
∈ 1,2,3 ..., and Y } representation space index；usAnd σsShow normalized mean value and standard deviation square value；C is a small constant,
The case where to avoid denominator being zero.μpAnd σpIt may be calculated:
W (r, z) | r=-R ..., R；Z=-Z ..., Z } unit volume Gauss window can be defined as；R and Z are set as
3, to ensure that window size is fairly small, smaller window size provides higher performance.This step passes through part normalization rule
Model SCIs image edge information.
Step 3: the distribution estimation of L square
This step is fitted the probability distribution of the image border SCIs figure by L square, and coefficient characterizes edge feature.L- square is one
Group is used to summarize the statistical series of probability distribution shape, including generalized Gaussian distribution, laplacian distribution, and L- moments estimation is a kind of
The statistical parameter estimation method of function admirable has stronger robustness to little deviation model, and the formula of square is defined as follows:
ρ1=η0
ρ2=2 η1-η0
ρ3=6 η2-6η1+η0
ρ4=20 η3-30η2+12η1-η0
L=(η1,η2,...,ηi), i=4
In addition, the distortion of image will affect the Multi-scale model of image, multi-scale information is introduced and is commented without ginseng picture quality
Preferable performance can be obtained in valence.To distortion SCI parameter attribute vector by
Feature=[LD1,LD2,...,LDi], i=7
The extraction that this step carries out edge graph using down-sampled method, and feature extraction is carried out on three scales.Its
Middle symbol D indicates the response of DOG, and L is L- square fitting coefficient.
Step 4: image quality evaluation model training
The one group of n dimension edge feature vector and its corresponding visual quality evaluation mark that this step is generated based on above-mentioned steps
Label, the calculating Environmental Evaluation Model of SCIs is established using homing method.In the training stage, edge feature and its corresponding subjectivity are utilized
Difference mean value Opinion Score (DMOS) value establishes regression model.Forecast period calculates test SCIs image through the above steps
Obtained edge feature vector, is input in training pattern, predicts SCIs in image quality evaluation score.
Wherein model chooses ABPNN frame, and particular content is, firstly, we will guarantee the quantity Q of Weak Classifier, and
By the O of the SCIs in training settrainjSubjective differences mean value Opinion Score (DMOS) is mapped to [0,1].AdaBoosting algorithm
Multiple relatively weak classifiers combinations are got up, a stronger regression machine is configured to, there is very big mention in arithmetic accuracy
Height, i-th of Weak Classifier is in training set training ΩtrainAnd Otrainj.Therefore, it can estimate O' in each training settrainiIt is pre-
Survey output.Its formula is as follows:
The calculating training error of each training set, this is considered as Γ distribution.Γ is in training set ΩtrainI-th of expression
Weak Classifier:
Using AdaBoosting algorithm, commented by the original quality evaluation score to i-th of Weak Classifier with forecast quality
Difference between valence score is integrated, and estimates its error of quality appraisement Γ and corresponding distribution.
Corresponding weight is converted by the weight of single Weak Classifier using convex function, advantageously ensures that lower weak typing
The weight of device is bigger, and the weight of lower Weak Classifier is smaller.The i component value λ of Weak ClassifieriIt can be expressed as
Wherein b and c is small normal number.
Therefore, final SCIs image quality evaluation score output O' can be calculated in conjunction with prediction output phase by weight, be determined
Justice are as follows:
Forecast period calls the model trained, and the n that SCIs image is obtained through the above steps ties up edge feature vector
Input model obtains its corresponding image quality evaluation score.
Claims (2)
1. a kind of screen content image based on edge feature information without ginseng quality evaluating method, including the following steps:
The first step is calculated by DOG algorithm and obtains the mapping of the image border SCIs；
Second step, the edge map information that will acquire carries out local normalization, in the case where given SCIs, using local average
Subtraction and the normalized method of division calculate normalization pixel；
Third step carries out the extraction of edge feature vector, using the fitting of L- square distribution estimation method from part normalization edge graph
Extract marginal information.
4th step returns device using ABPNN and quality is perceived maps feature vectors to subjective quality score, L- square is distributed and is estimated
SCIs image n dimension its subjective quality assessment score of edge feature vector sum of extraction is put into training in ABPNN network, in training rank
Section establishes regression model using edge feature vector and its corresponding subjective differences mean value Opinion Score (DMOS) value；Forecast period,
The feature vector that SCIs is calculated will be tested to be input in the model of study, predict SCIs image quality evaluation score.
2. the method according to claim 1, wherein the training stage in step 4, using following method:
Guarantee the quantity Q of Weak Classifier, and the subjective differences mean value Opinion Score (DMOS) of the SCIs in training set is mapped to [0,
1]；Using AdaBoosting algorithm, pass through the original quality evaluation score and forecast quality evaluation point to i-th of Weak Classifier
Difference between number is integrated, and estimates its error of quality appraisement Γ and corresponding distribution；Using convex function by single Weak Classifier
Weight is converted into corresponding weight, advantageously ensures that the weight of lower Weak Classifier is bigger, the weight of lower Weak Classifier
It is smaller；Final SCIs image quality evaluation score output is calculated in conjunction with prediction output phase by weight.
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| CN113222902A (en) * | 2021-04-16 | 2021-08-06 | 北京科技大学 | No-reference image quality evaluation method and system |
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