CN104867138A - Principal component analysis (PCA) and genetic algorithm (GA)-extreme learning machine (ELM)-based three-dimensional image quality objective evaluation method - Google Patents
Principal component analysis (PCA) and genetic algorithm (GA)-extreme learning machine (ELM)-based three-dimensional image quality objective evaluation method Download PDFInfo
- Publication number
- CN104867138A CN104867138A CN201510232529.0A CN201510232529A CN104867138A CN 104867138 A CN104867138 A CN 104867138A CN 201510232529 A CN201510232529 A CN 201510232529A CN 104867138 A CN104867138 A CN 104867138A
- Authority
- CN
- China
- Prior art keywords
- elm
- sample
- learning machine
- network
- matrix
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
- G06T7/41—Analysis of texture based on statistical description of texture
- G06T7/44—Analysis of texture based on statistical description of texture using image operators, e.g. filters, edge density metrics or local histograms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
Abstract
The invention relates to a principal component analysis (PCA) and genetic algorithm (GA)-extreme learning machine (ELM)-based three-dimensional image quality objective evaluation method. The method includes the following steps that: evaluation data samples are obtained through a subjective test, and a training image sample and a testing image sample are selected, and feature extraction and dimension reduction are performed on the training image sample through adopting PCA, and sample data xi are projected to an effective sub-space formed by selected feature vectors, so that projection coefficients can be obtained; an extreme learning machine (ELM) network is established, the projection coefficients of the training sample are inputted into the ELM network, and after the input weight and threshold value of a hidden layer are optimized, a relationship between an original image and a subjective evaluation value is established, so that an ELM network structure is obtained; the weight and threshold value of the hidden layer of an extreme learning machine (ELM) are optimized through a genetic algorithm (GA); and image quality evaluation is performed on a certain three-dimensional image in the test sample. With the CA and GA-ELM-based three-dimensional image quality objective evaluation method of the invention adopted, the quality of three-dimensional images can be evaluated more accurately and effectively.
Description
Art
The invention belongs to image processing field, relate to image quality evaluating method and improve and optimizate, especially relate to the application of PCA and GA-ELM in stereo image quality objective evaluation.
Background technology
In recent years, along with the develop rapidly of computer science and technology, the interest of people to 3 d visualization is more and more denseer, and computer graphics is also developed rapidly in every profession and trade, and computing machine enters the three-dimensional epoch.From the fusion of computer graphics, computer vision, multimedia and relevant other technologies, multiple new visual media is rapidly developed, such as three-dimensional video-frequency and free viewpoint video, bring new visual experience to the mankind, surmounted traditional 2D video far away
[1].Along with the continuous maturation of stereoscopic imaging technology, stereo product is really come in the life of people, from then on the reproduction in the 3D world, is no longer only dream.Stereo display technique is in outdoor
[2-4]application also more and more universal.
Present stage, carry out stereo image quality and evaluate mainly people and utilize human eye to obtain the steric information of image, then various steric information is carried out integrating and is made us experience three-dimensional sensation by brain, and then analyzes the quality judging stereo image quality.Under normal circumstances, two kinds of method stereoscopic image quality are adopted to evaluate: subjective assessment and objective evaluation.Subjective assessment is mainly by selecting a large amount of testers, the stereoeffect of the stereo-picture seen according to observer, sharpness and the standards of grading of picture quality formulated according to ITU in advance, carry out grade scoring to the stereo-picture sample chosen according to subjective personal feeling.Although this method can obtain picture appraisal more accurately, its spended time is long, cost is high, is difficult to operation.Therefore, set up a set of stereo image quality objective evaluation standard that is effective, specification and become a main research in stereoscopic imaging technology field.Wherein method for objectively evaluating is mainly through extracting the corresponding steric information index of stereo-picture, then the mathematical model of mathematical formulae or foundation is utilized to describe the subjective feeling of people's stereoscopic image, and then pass judgment on the quality grade of stereo-picture, the method is time saving and energy saving, operability is stronger.Therefore setting up the objective evaluation model consistent with subjective evaluation result is following development trend, and Chinese scholars is also to this has been a series of exploration.
At first, people mainly evaluate the quality grade of stereo-picture by the evaluation method introducing plane picture quality.In the past in decades, the two-dimensional image quality evaluation method that researcher proposes has: Y-PSNR (PSNR), square error (MSE), structural similarity (SSIM) index
[5]and based on the IWSSIM algorithm of information content weights
[6]etc..You
[7]deng people, some 2D image quality evaluating methods are wherein applied in the middle of stereo-picture, and corresponding com-parison and analysis has been carried out to its performance.
Hewage
[8]deng people propose first compute depth figure profile and by its binaryzation, then evaluated by PSNR method stereoscopic image quality, the document describes a kind of half reference image quality appraisement algorithm model based on depth map marginal information, substantially can meet the visual experience of human eye.Ding
[9]a kind of full reference image quality appraisement algorithm model utilizing the partial statistics correlativity of image is described Deng people, the method is extracted the local correlations of image in wavelet field and is collected an objective assessment score, not only has very high precision but also basically identical with subjective evaluation result by the known the method for test result; User's visually-perceptible is affected by summing up
[10]some factors: level of cross talk, video camera baseline and scene content, propose a kind of evaluating objective quality algorithm based on stereo-picture crosstalk perception accordingly, first this algorithm calculates depth map from stereo-picture, then obtains corresponding weight value in SSIM map according to original and impaired image.At document
[11]in, the people such as author Akhter describe a kind of based on the segmentation local characteristics of image and the non-reference picture Objective Quality Assessment algorithm of parallax information, the Inspiration Sources of the method depends on the local feature of image as the non-edge of nonplanar marginal information, plane in the perceptual distortion of the vision system (Human Visual System, HVS) of the mankind and any stereo display and parallax information; Document
[12]propose a kind of stereo image quality evaluation method based on disparity map, the method, using the criterion of subjective experiment as steric information quality assessment, uses vertical missing and the time consistency of depth map dimensioned plan picture;
For the quality assessment of stereo-picture, although Recent study person has carried out a series of further investigation to human visual system, but due to the complicacy of human eye system, the mankind are still more shallow to the cognition of human visual system, so people still cannot propose and human eye subjective feeling on all four stereo image quality objective evaluation algorithm.For this reason, some scholars consider neural network to apply to stereo image quality objective evaluation aspect, and achieve good effect.Document
[13]first the validity feature of stereo-picture is extracted by independent component analysis (ICA), then a kind of classifying and identifying system that can be used in stereo image quality objective evaluation by support vector machine (BT-SVM) algorithm design based on binary tree; At document
[14]in, the people such as author Gu Shanbo have highly stable characteristic according to the singular value of image, then in conjunction with the subjective vision apperceive characteristic of stereo-picture, construct a kind of objective evaluation algorithm model based on SVR and stereo-picture vision perception characteristic.First the method extracts left images singular value, and the distortion situation then according to each image merges, and finally utilizes SVR model to export the objective evaluation value of stereo-picture; Document
[15]adopt Y-PSNR and structural similarity to carry out Description Image quality, devise a kind of stereo image quality classifying and identifying system based on neural network and support vector.
Neural network objective evaluation model has parallel organization and the Parallel Implementation ability of height, and effectively can process nonlinear problem, thus neural network can obtain better effects in stereo image quality objective evaluation.Document
[16]propose the application of extreme learning machine in stereo image quality is evaluated, extreme learning machine (ELM) can solve the problems such as traditional neural network training speed slowly, is easily absorbed in local minimum and Generalization Capability is low for the objective evaluation of stereo image quality.ELM develops on the basis of neural networks with single hidden layer, the weight between its input layer and hidden layer and the threshold value of hidden layer without the need to iterating, as long as random assignment.Therefore the net result of extreme learning machine has very large difference, needs to ask its mean value as net result by repeatedly running, but this method but misses the selection of optimal result.
List of references
[1]Smolic,K.Mueller,P.Merkle,C.Fehn,P.Kauff,P.Eisert,T.Wiegand.3D video and free view-point video—technologies,applications and MPEG standards[C].International Conference on Multimedia and Expo,Toronto,Ontario,Canada,2006:2161-2164.
[2]Reitterer J,Fidler F,Schmid G,et al.Design and evaluation ofa large-scale autostereoscopic multi-view laser display for outdoor applications[J].Optics Express,2014,22(22):27063-27068.
[3]HIROTSUGU Y,MAKOTO K,SYUJI M,et al.Enlargement ofviewing area of stereoscopic full-color led display by use ofaparallax barrier[J].Appl.Opt.,2002,41(32):6907-6919.
[4]Baselgia C,Bosse M,Zlot R,et al.Solid Model Reconstruction ofLarge-Scale Outdoor Scenes from 3D Lidar Data[C].Field and Service Robotics.Springer Berlin Heidelberg,2014:541-554.
[5]Z.Wang,A.C.Bovik,H.R.Sheikh and E.P.Simoncalli.Image quality assessment:
From error visibility to structural similarity[J].IEEE Trans.Image Process,2004,13(4):600-612.
[6]Wang Z,Li Q.Information content weighting for perceptual image quality assessment[J].Image Processing,IEEE Transactions on,2011,20(5):1185-1198.
[7]You J,Xing L,Perkis A,et al.Perceptual quality assessment for stereoscopic images based on 2D image quality metrics and disparity analysis[C].Proc.of International Workshop on Video Processing and Quality Metrics for Consumer Electronics,Scottsdale,AZ,USA,2010.
[8]Hewage C T E R,Martini M G.Reduced-reference quality metric for 3D depth map transmission[C].3DTV-Conference:The True Vision-Capture,Transmission and Display of 3D Video(3DTV-CON),2010,IEEE,2010:1-4.
[9]Ding Y,Wang S,and Zhang D.Full-reference image quality assessment using statistical local correlation[J].Electronics Letters,2014,50(2):79-81.
[10]Xing L,You J,Ebrahimi T,et al.A perceptual quality metric for stereoscopic crosstalk perception[C].Image Processing(ICIP),201017th IEEE International Conference on,IEEE,2010:4033-4036.
[11]Akhter R,Sazzad Z M P,Horita Y,et al.No-Reference Stereoscopic Image Quality Assessment[C].Proc.IS&T/SPIE,Electronic Imaging,San Jose,California,USA,Feb 15,2010.
[12]KIMDH,MINDB,OHJ H,et al.Depth map quality metric for three-dimensional video[C].Proceedings ofthe SPIE Volume 7237:Stereoscopic Displays andApplications XX,San Jose,USA,2009:29-34.
[13] the emerald green application of .ICA and BT-SVM in stereo image quality evaluation system [D] of Cheng Jin. Tianjin: University Of Tianjin, 2013,1-66.
[14] Gu Shanbo, Shao Feng, Jiang Gangyi, etc. based on the three-dimensional image objective quality evaluation model [J] of support vector regression. electronics and information journal, 2012,34 (2): 368-374.
[15] Tong Yubing, Zhang Qishan, evergreen, etc. based on the image quality evaluation model [J] of NN and SVM. BJ University of Aeronautics & Astronautics's journal, 2006,32 (9): 1031-1034.
[16] Wang Guanghua, Li Sumei, Zhu Dan etc. the extreme application of learning machine in stereo image quality objective evaluation [J]. optoelectronic laser, 2014,25 (9): 1837-1842.
Summary of the invention
The present invention is intended to the above-mentioned deficiency overcoming prior art, there is provided a kind of and carry out space dimension-reduction treatment by PCA algorithm stereoscopic image sample, by genetic algorithm, the application of extreme learning machine in stereo image quality objective evaluation is optimized, makes the ELM algorithm after optimization evaluate stereo image quality more accurately and effectively.Technical scheme of the present invention is as follows:
An objective evaluation method for quality of stereo images of Based PC A and GA-ELM, is characterized in that, comprises the following steps:
Step (1), first obtain evaluating data sample according to ITU-R BT.500 and ITU-R BT.1438 standard by subjective testing, choose training image sample and test pattern sample;
Step (2), PCA is adopted to carry out feature extraction and dimensionality reduction to the training image sample chosen in step (1) and test pattern sample, original training image sample and test pattern sample are converted into matrix variables, every width stereo-picture has corresponding subjective assessment mark for its name, produce traindata and trainlabel file, traindata file is the matrix variables representing original image, the matrix variables that trainlabel file forms for every width image subjective assessment mark, represent the subjective assessment mark of every width figure reality, initial input in reduction process is traindata matrix, it is the actual matrix parameter of sample data matrix X, centralization process is done to sample data matrix X, calculate the covariance matrix R of sample
x, calculate covariance matrix R
xeigenwert and proper vector, determine principal component number, by each sample data x
iobtain projection coefficient in effective subspace that the proper vector projecting to selection forms, after normalization, generate the matrix variables traindata2 after dimensionality reduction,
Step (3), set up extreme learning machine ELM network, the projection coefficient of the training sample obtained through step (2) is input to ELM network, the input end of network is matrix variables traindata2, output terminal is matrix variables trainlabel, after layer input weights and threshold to be concealed is optimized, set up the relation between original image and subjective assessment value, obtain ELM network structure; Be optimized by the hidden layer weights and threshold of Genetic Algorithms to extreme learning machine ELM, optimizing process is as follows:
1. initialization of population is carried out: real coding is adopted to population at individual coding, population at individual comprises all weights and the threshold value of the network structure of extreme learning machine ELM, when network structure is determined, an extreme learning machine network determined by network structure, weights and threshold value can be built.
2. fitness function calculates: select the data error absolute value sum of training sample as population at individual fitness;
3. calculate the fitness sum of all individualities in population, calculate the relative adaptability degrees of each individuality in population, and it is selected as the probability of parent procreation descendants as this individuality;
4. utilize roulette method, generate random number t, a t ∈ (0,1), and using this value as each population at individual by the number of times selected, the individuality that fitness is large, selected probability is larger, and its gene constantly will expand in population;
5. utilize interlace operation for the extreme weight a of learning machine network and the optimization of threshold value b, arithmetic crossover operator is selected in interlace operation, then obtains new individuality according to the probability calculated a pair individuality of recombinating simultaneously;
6. perform mutation operation, if do not reach the stopping criterion for iteration of setting, then return step 2., if reach, terminate whole iterative process, complete the optimization of ELM network structure;
Step (4), for certain stereo-picture in test sample book, utilize the ELM network set up to carry out the evaluation of picture quality, export evaluation result.
Experimental result and Data Comparison show, the present invention utilized PCA algorithm stereoscopic image sample to carry out space dimension-reduction treatment before this, by comparing the performance of ELM algorithm and BP, SVM algorithm, therefrom known ELM is better than BP and SVM algorithm in stereo image quality discriminator, be optimized by the parameter of genetic algorithm to ELM algorithm again, compare the graph of errors of GA-ELM and ELM, can find out that GA-ELM is obviously better than ELM network in stereo image quality evaluation.Generally, this extreme learning machine adopts genetic algorithm (GA) to be optimized to obtain optimal weights and threshold value to the weight of the random assignment of extreme learning machine and threshold value, experimental result shows, be that the stereo-picture test sample book of excitation function to different brackets is tested with sigmoid, its correct recognition rata can reach 95.85%, higher than without the ELM discrimination optimized, there is actual application value.
Accompanying drawing explanation
Fig. 1 source images " family "
Fig. 2 source images " girl "
Fig. 3 source images " river "
Fig. 4 source images " flower "
Fig. 5 degraded image " family "
Fig. 6 degraded image " girl "
Fig. 7 degraded image " river "
Fig. 8 degraded image " flower "
Fig. 9 stereo image quality objective evaluation block diagram
The part major component of Figure 10 PCA
Figure 11 GA optimizes ELM network algorithm flow process.
Embodiment
Stereo-picture selected by the design is all from broadband wireless communications and three-dimensional imaging research institute image data base.All stereo image qualities all according to two standard: BT-500 and BT.1438-2000 that the subjective assessment of International Telecommunications Union (ITU) (ITU) stereoscopic image quality is advised, are divided into 5 grades: fabulous, good, general, poor, non-constant by the stereo-picture in database.
The design chooses 362 width stereo-picture samples altogether, and each sample image resolution is 2560 × 1024.Wherein 4 width are source stereo-picture (family, girl, river, flower), as shown in figures 1-4, all the other degraded images being covering 5 quality assessment grades, degraded image produces by adding dissimilar noise to source images or compressing, as shown in figures 5-8, Fig. 5 is through the 4 sequential Stereoscopic images that parallax degrades and JPEG compresses, Fig. 6 is the 1 sequential Stereoscopic image through JPEG compression, and Fig. 7 is the 2 sequential Stereoscopic images degraded through parallax, and Fig. 8 is the 3 sequential Stereoscopic images that left view carries out JPEG compression.Choose 121 width images in 362 width stereo-picture samples as training sample, 241 width images are as test sample book.The design is that Intel (R) Core (TM) the i5-2380P hardware environment being 3.10GHz by CPU is supported, the execution environment of ELM, GA and BP is the execution environment of matlab7.13, SVM is C language.
Below in conjunction with technical scheme process in detail:
One, obtain evaluating data sample by subjective testing, choose training sample and test sample book through repetition test.
Tested comprise specialty tested and amateur tested, all there is normal parallax stereoscopic sensation, totally 20 tested, be respectively school postgraduate and undergraduate, the male sex 11, women 9, be engaged in tested totally 16 people of steric information treatment research, be engaged in tested totally 4 people of other directions research.For the ease of intuitivism apprehension the design, provide stereo image quality objective evaluation block diagram, as shown in Figure 9.
Two, feature extraction and dimensionality reduction are carried out for the training sample chosen and test sample book.Concrete steps are as follows:
Centralization process is done to sample data matrix:
φ
i=x
i-m
i(2)
Wherein, a given p sample data, each sample vector size is m × n, and its sample matrix is X=(x
1, x
2..., x
p)
t, x here
ibe a column vector of the mn dimension of i-th composition of sample, centralization process done to sample data matrix X, utilizes formula (1) to obtain the average of sample data, by formula (2), process is carried out to sample matrix and obtain matrix A=[φ
1, φ
2..., φ
p].
Calculate the covariance matrix R of sample
x:
Calculate covariance matrix R
xeigenwert and proper vector:
|R
x-λI|=0 (4)
Determine principal component number, by each sample data x
iprojection coefficient is obtained in effective subspace that the proper vector projecting to selection forms:
Wherein σ is the contribution rate of accumulative total of each principal ingredient information, it is the ratio of eigenwert sum and all eigenwert sums chosen, the number of major component is determined by contribution rate of accumulative total, and in effective subspace of forming of the proper vector that each sample data is projected to selection.The part major component of PCA as shown in Figure 10.
Three, compare the performance of algorithms of different in three-dimensional image objective is evaluated after training sample and test sample book input heterogeneous networks, adopt genetic algorithm to be optimized extreme learning machine and and other method comparison simultaneously
Obtain the objective evaluation value of test pattern in projection coefficient input ELM, SVM and BP network of training sample and test sample book, add up and analyze the performance of ELM, SVM and BP stereoscopic image Objective Quality Assessment.
, be optimized by genetic algorithm (GA) parameter to ELM algorithm the correlation parameter obtaining GA-ELM neural network algorithm simultaneously, and compare the performance difference of itself and other algorithm.
GA-ELM optimizing process is as follows:
1. carry out initialization of population, current individual UVR exposure method mainly contains: binary law, real number method etc., and the coding of population at individual adopts real coding, namely using the value of a real number string as each individuality here.
2. fitness function calculates, and selects the data error absolute value sum of training set as population at individual fitness function here
Wherein n represents the output node number of neural network,
represent predicting the outcome of training set, T={t
1, t
2..., t
nthe actual value of training set, k is weighting coefficient.
3. select operation, adopt the operation of simulation roulette, concrete steps are: the fitness sum calculating all individualities in population, calculate the relative adaptability degrees of each individuality in population, and it is selected as the probability of parent procreation descendants as this individuality.
4. utilize roulette method, generate random number t, a t ∈ (0,1), and using this value as each population at individual by the number of times selected.Simultaneously known, the individuality that fitness is large, selected probability is larger, and its gene constantly will expand in population.
5. interlace operation is for the extreme weight of learning machine network and the optimization of threshold value, and arithmetic crossover operator is selected in interlace operation, then obtains new individuality according to the probability calculated a pair individuality of recombinating simultaneously.By following formula, two new individualities are obtained to the individuality of restructuring for each.
y
1=p
1×α+p
2×(1-α) (7)
y
2=p
1×(1-α)+p
2×α (8)
Wherein, p
1, p
2represent two individualities mutually matched; y
1, y
2new two individualities produced; α is a number being positioned at interval (0,1), i.e. crossover probability.
6. mutation operation, supposes to choose i-th individual jth gene b
ijmake a variation, then concrete grammar formula is as follows:
Wherein, b
maxand b
mingene b respectively
ijthe upper bound and lower bound; F (g)=r
0(1-g/G
max)
2, r
0represent a random value, G
maxrepresent the maximal value of evolution iterations, g represents current evolution iterations; R is the random value being positioned at interval (0,1).
Genetic algorithm is adopted to optimize extreme learning machine network and be mainly divided into: the determination of extreme learning machine network, genetic algorithm optimization and extreme learning machine prediction Output rusults 3 parts.Wherein, the initial number of GA population at individual can be determined according to the structure of extreme learning machine network.All weights and the threshold value of extreme learning machine network can be optimized by GA, and each individuality in population includes its weight and threshold value, the fitness value of each individuality is calculated according to fitness function, and then carry out selecting, crossover and mutation operation finds optimum individual, the namely optimum solution of space search.Extreme learning machine prediction Output rusults just by the individuality of optimum is carried out assignment to the initial weight of network and threshold value, and then predicts test sample book result.According to the above, optimize ELM algorithm flow as shown in figure 11 with GA.
In the network parameter of this experiment, the parameter determination process of ELM is fairly simple, only need determine excitation function and hidden layer node number, GA-ELM is weight by genetic algorithm optimization ELM and threshold value, the iterations getting genetic algorithm in experiment is 100, population scale is 30, and crossover probability is 0.3, and mutation probability is 0.1.Choose different excitation functions (sigmoid function, sine function, hardlim function, triangular basis (tribas) function, radial basis (radbas) function), then the initial value of the hidden layer nodes of each excitation function is set to 10, and constantly increase hidden layer nodes with 10 for the cycle, 50 random tests are carried out in often kind of combination, add up the average recognition rate obtained, the impact of ELM stereoscopic image Objective Quality Assessment precision when analysis excitation function is different with the number of hidden nodes.And the Selection of kernel function radial basis function of SVM, wherein punish that the method that parameter C and nuclear parameter γ rolls over cross validation by 5-chooses optimum training parameter, C=128 is got in this experiment, γ=0.25.For BP, hidden layer uses transport function tan-sigmoid, output layer uses a linear transfer function, and training objective error is set is less than 0.00004, the number of hidden layer node is initialized as 10, increase progressively 5 at every turn, and choose optimum number by the method for cross validation, then carry out 50 experiments in each case at random and best average result is gathered.
In order to the performance of verification system more intuitively, table 1 and table 2 list ELM, SVM and BP respectively about the recognition result of four kinds of stereo-pictures and their accuracys rate in all test sample books.As shown in Table 1, the correct Classification and Identification rate of test of ELM obviously will be better than SVM, and reach more than 93%, and BP algorithm is due to the limitation of self, it is tested correct Classification and Identification rate and is only 84.65%.In pace of learning, because BP adopts gradient descent method adjustment weights, its pace of learning is caused to be starkly lower than ELM and SVM algorithm.As shown in Table 2, adopt ELM stereoscopic image quality to carry out objective evaluation substantially to conform to the actual subjective assessment mark of stereo-picture, simultaneously by compared with the recognition result of SVM, known ELM performance in stereo image quality objective evaluation is comparatively better than SVM, and Comparatively speaking traditional BP algorithm effect is poor.
For confirming the validity of GA-ELM in stereo image quality objective evaluation, choosing 241 stereo-pictures as test sample book, adopting GA-ELM (excitation function is sigmoid) and ELM (sigmoid) algorithm to predict.For this gathers the predicated error of GA-ELM (sigmoid) and ELM (sigmoid) algorithm and precision, as shown in table 3.As shown in Table 3, the hidden layer nodes that GA-ELM network needs is less, and the measuring accuracy that GA-ELM network obtains is higher, is up to 95.85%, confirms the practicality of GA-ELM sorter.Table 4 lists the identification situation of GA-ELM to source images girl and corresponding compression degraded image, and wherein girl5% represents and compresses by ratio of compression 5% source images girl.As shown in Table 4, when the compressing by ratio of compression 10%, 30%, 85% of source images girl, the objective evaluation value of sorter is different from subjective assessment value, may select to meet all sample requirements because misclassification sample itself exist interfere information or sorter inherent parameters for these image patterns that can correctly not identify, and sample image also can introduce error when subjective assessment.
Table 5 gives the optimal identification rate of ELM, GA-ELM and other three kinds of common classification recognition systems.As shown in Table 5, the discrimination of GA-ELM algorithm obviously will be better than other various methods, and ELM algorithm model also slightly will be better than first three methods.Thus ELM and the GA-ELM algorithm model of Based PC A that the design proposes has very large actual value in stereo image quality evaluation.
The Performance comparision that table 1ELM, SVM and BP algorithm is evaluated about stereo image quality
The discrimination that table 2ELM, SVM and BP evaluate about stereo image quality
Table 3GA-ELM degrades about source images girl and compression the performance of figure
The optimal situation of table 4GA-ELM and ELM under different excitation function
The discrimination that table 5 distinct methods is evaluated about stereo image quality.
Claims (1)
1. an objective evaluation method for quality of stereo images of Based PC A and GA-ELM, is characterized in that, comprises the following steps:
Step (1), obtain evaluating data sample by subjective testing, choose training image sample and test pattern sample;
Step (2), PCA is adopted to carry out feature extraction and dimensionality reduction to the image pattern chosen in step (1), original training image sample and test pattern sample are converted into matrix variables, every width stereo-picture has corresponding subjective assessment mark for its name, produce traindata and trainlabel file, traindata file is the matrix variables representing original image, the matrix variables that trainlabel file forms for every width image subjective assessment mark, represent the subjective assessment mark of every width figure reality, initial input in reduction process is traindata matrix, it is the actual matrix parameter of sample data matrix X, centralization process is done to sample data matrix X, calculate the covariance matrix R of sample
x, calculate covariance matrix R
xeigenwert and proper vector, determine principal component number, by each sample data x
iobtain projection coefficient in effective subspace that the proper vector projecting to selection forms, after normalization, generate the matrix variables traindata2 after dimensionality reduction,
Step (3), set up extreme learning machine ELM network, the projection coefficient of the training sample obtained through step (2) is input to ELM network, the input end of network is matrix variables traindata2, output terminal is matrix variables trainlabel, after layer input weights and threshold to be concealed is optimized, set up the relation between original image and subjective assessment value, obtain ELM network structure; Be optimized by the hidden layer weights and threshold of Genetic Algorithms to extreme learning machine ELM, optimizing process is as follows:
1. initialization of population is carried out: real coding is adopted to population at individual coding, population at individual comprises all weights and the threshold value of the network structure of extreme learning machine ELM, when network structure is determined, an extreme learning machine network determined by network structure, weights and threshold value can be built.
2. fitness function calculates: select the data error absolute value sum of training sample as population at individual fitness;
3. calculate the fitness sum of all individualities in population, calculate the relative adaptability degrees of each individuality in population, and it is selected as the probability of parent procreation descendants as this individuality;
4. utilize roulette method, generate random number t, a t ∈ (0,1), and using this value as each population at individual by the number of times selected, the individuality that fitness is large, selected probability is larger, and its gene constantly will expand in population;
5. utilize interlace operation for the extreme weight a of learning machine network and the optimization of threshold value b, arithmetic crossover operator is selected in interlace operation, then obtains new individuality according to the probability calculated a pair individuality of recombinating simultaneously;
6. perform mutation operation, if do not reach the stopping criterion for iteration of setting, then return step 2., if reach, terminate whole iterative process, complete the optimization of ELM network structure;
Step (4), for certain stereo-picture in test sample book, utilizes the ELM network set up to carry out the evaluation of picture quality, exports evaluation result.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510232529.0A CN104867138A (en) | 2015-05-07 | 2015-05-07 | Principal component analysis (PCA) and genetic algorithm (GA)-extreme learning machine (ELM)-based three-dimensional image quality objective evaluation method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510232529.0A CN104867138A (en) | 2015-05-07 | 2015-05-07 | Principal component analysis (PCA) and genetic algorithm (GA)-extreme learning machine (ELM)-based three-dimensional image quality objective evaluation method |
Publications (1)
Publication Number | Publication Date |
---|---|
CN104867138A true CN104867138A (en) | 2015-08-26 |
Family
ID=53912953
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510232529.0A Pending CN104867138A (en) | 2015-05-07 | 2015-05-07 | Principal component analysis (PCA) and genetic algorithm (GA)-extreme learning machine (ELM)-based three-dimensional image quality objective evaluation method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104867138A (en) |
Cited By (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105608700A (en) * | 2015-12-24 | 2016-05-25 | 广州视源电子科技股份有限公司 | Photo screening method and system |
CN106649964A (en) * | 2016-10-17 | 2017-05-10 | 贵州大学 | Predicting method of aluminum alloy die pressed casting grain size based on GA-ELM algorithm |
CN106845711A (en) * | 2017-01-22 | 2017-06-13 | 南方电网科学研究院有限责任公司 | The processing method and processing unit of power supply reliability data |
CN106897957A (en) * | 2017-02-22 | 2017-06-27 | 南京信息工程大学 | A kind of automatic weather station real time data method of quality control based on PCA and PSO ELM |
CN107256453A (en) * | 2016-12-05 | 2017-10-17 | 东北大学 | A kind of quality of hollow billet forecasting procedure based on improvement ELM algorithms |
CN107392235A (en) * | 2017-07-06 | 2017-11-24 | 西华大学 | A kind of contact net equipment sorting technique based on GA ELM |
CN107423911A (en) * | 2017-08-02 | 2017-12-01 | 中国科学院上海高等研究院 | Software Evaluating Degree of Success method/system, computer-readable recording medium and equipment |
CN107679543A (en) * | 2017-02-22 | 2018-02-09 | 天津大学 | Sparse autocoder and extreme learning machine stereo image quality evaluation method |
CN108509487A (en) * | 2018-02-08 | 2018-09-07 | 杨睿嘉 | Image search method, equipment and the storage medium of cortex model are provided based on pulse |
CN109949277A (en) * | 2019-03-04 | 2019-06-28 | 西北大学 | A kind of OCT image quality evaluating method based on sequence study and simplified residual error network |
CN110221809A (en) * | 2018-03-01 | 2019-09-10 | 钰创科技股份有限公司 | It collects and the method for analysis data and relevant device |
CN110458887A (en) * | 2019-07-15 | 2019-11-15 | 天津大学 | A kind of Weighted Fusion indoor orientation method based on PCA |
CN110473140A (en) * | 2019-07-18 | 2019-11-19 | 清华大学 | A kind of image dimension reduction method of the extreme learning machine based on figure insertion |
CN110766658A (en) * | 2019-09-23 | 2020-02-07 | 华中科技大学 | Non-reference laser interference image quality evaluation method |
CN111027589A (en) * | 2019-11-07 | 2020-04-17 | 成都傅立叶电子科技有限公司 | Multi-division target detection algorithm evaluation system and method |
CN113486731A (en) * | 2021-06-17 | 2021-10-08 | 国网山东省电力公司汶上县供电公司 | Abnormal state monitoring method for power transmission equipment |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102982535A (en) * | 2012-11-02 | 2013-03-20 | 天津大学 | Stereo image quality evaluation method based on peak signal to noise ratio (PSNR) and structural similarity (SSIM) |
-
2015
- 2015-05-07 CN CN201510232529.0A patent/CN104867138A/en active Pending
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102982535A (en) * | 2012-11-02 | 2013-03-20 | 天津大学 | Stereo image quality evaluation method based on peak signal to noise ratio (PSNR) and structural similarity (SSIM) |
Non-Patent Citations (3)
Title |
---|
吴限光等: "《基于遗传神经网络的立体图像的客观评价》", 《信息技术》 * |
张尤赛等: "《基于视觉敏感度的JPEG图像质量评价》", 《计算机工程》 * |
王光华等: "《极端学习机在立体图像质量客观评价中的应用》", 《光电子 激光》 * |
Cited By (25)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105608700B (en) * | 2015-12-24 | 2019-12-17 | 广州视源电子科技股份有限公司 | Photo screening method and system |
CN105608700A (en) * | 2015-12-24 | 2016-05-25 | 广州视源电子科技股份有限公司 | Photo screening method and system |
CN106649964A (en) * | 2016-10-17 | 2017-05-10 | 贵州大学 | Predicting method of aluminum alloy die pressed casting grain size based on GA-ELM algorithm |
CN106649964B (en) * | 2016-10-17 | 2020-02-04 | 贵州大学 | GA-ELM algorithm-based aluminum alloy die casting grain size prediction method |
CN107256453A (en) * | 2016-12-05 | 2017-10-17 | 东北大学 | A kind of quality of hollow billet forecasting procedure based on improvement ELM algorithms |
CN107256453B (en) * | 2016-12-05 | 2020-10-23 | 东北大学 | Capillary quality forecasting method based on improved ELM algorithm |
CN106845711A (en) * | 2017-01-22 | 2017-06-13 | 南方电网科学研究院有限责任公司 | The processing method and processing unit of power supply reliability data |
CN106897957A (en) * | 2017-02-22 | 2017-06-27 | 南京信息工程大学 | A kind of automatic weather station real time data method of quality control based on PCA and PSO ELM |
CN107679543A (en) * | 2017-02-22 | 2018-02-09 | 天津大学 | Sparse autocoder and extreme learning machine stereo image quality evaluation method |
CN106897957B (en) * | 2017-02-22 | 2020-08-04 | 南京信息工程大学 | Automatic weather station real-time data quality control method based on PCA and PSO-E L M |
CN107392235A (en) * | 2017-07-06 | 2017-11-24 | 西华大学 | A kind of contact net equipment sorting technique based on GA ELM |
CN107423911A (en) * | 2017-08-02 | 2017-12-01 | 中国科学院上海高等研究院 | Software Evaluating Degree of Success method/system, computer-readable recording medium and equipment |
CN108509487B (en) * | 2018-02-08 | 2022-09-30 | 杨睿嘉 | Image retrieval method, device and storage medium based on pulse-issued cortex model |
CN108509487A (en) * | 2018-02-08 | 2018-09-07 | 杨睿嘉 | Image search method, equipment and the storage medium of cortex model are provided based on pulse |
CN110221809A (en) * | 2018-03-01 | 2019-09-10 | 钰创科技股份有限公司 | It collects and the method for analysis data and relevant device |
CN110221809B (en) * | 2018-03-01 | 2023-12-29 | 钰创科技股份有限公司 | Method for collecting and analyzing data and related device |
CN109949277A (en) * | 2019-03-04 | 2019-06-28 | 西北大学 | A kind of OCT image quality evaluating method based on sequence study and simplified residual error network |
CN109949277B (en) * | 2019-03-04 | 2022-12-06 | 西北大学 | OCT image quality evaluation method based on sequencing learning and simplified residual error network |
CN110458887A (en) * | 2019-07-15 | 2019-11-15 | 天津大学 | A kind of Weighted Fusion indoor orientation method based on PCA |
CN110473140A (en) * | 2019-07-18 | 2019-11-19 | 清华大学 | A kind of image dimension reduction method of the extreme learning machine based on figure insertion |
CN110766658A (en) * | 2019-09-23 | 2020-02-07 | 华中科技大学 | Non-reference laser interference image quality evaluation method |
CN110766658B (en) * | 2019-09-23 | 2022-06-14 | 华中科技大学 | Non-reference laser interference image quality evaluation method |
CN111027589A (en) * | 2019-11-07 | 2020-04-17 | 成都傅立叶电子科技有限公司 | Multi-division target detection algorithm evaluation system and method |
CN111027589B (en) * | 2019-11-07 | 2023-04-18 | 成都傅立叶电子科技有限公司 | Multi-division target detection algorithm evaluation system and method |
CN113486731A (en) * | 2021-06-17 | 2021-10-08 | 国网山东省电力公司汶上县供电公司 | Abnormal state monitoring method for power transmission equipment |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104867138A (en) | Principal component analysis (PCA) and genetic algorithm (GA)-extreme learning machine (ELM)-based three-dimensional image quality objective evaluation method | |
CN104866864A (en) | Extreme learning machine for three-dimensional image quality objective evaluation | |
CN106462771A (en) | 3D image significance detection method | |
CN110555434B (en) | Method for detecting visual saliency of three-dimensional image through local contrast and global guidance | |
Shao et al. | Full-reference quality assessment of stereoscopic images by learning binocular receptive field properties | |
CN107085716A (en) | Across the visual angle gait recognition method of confrontation network is generated based on multitask | |
CN110211045A (en) | Super-resolution face image method based on SRGAN network | |
CN110060236B (en) | Stereoscopic image quality evaluation method based on depth convolution neural network | |
CN109272499A (en) | Non-reference picture quality appraisement method based on convolution autoencoder network | |
CN109360178A (en) | Based on blending image without reference stereo image quality evaluation method | |
CN104268593A (en) | Multiple-sparse-representation face recognition method for solving small sample size problem | |
CN108765414A (en) | Based on wavelet decomposition and natural scene count without referring to stereo image quality evaluation method | |
Yue et al. | Blind stereoscopic 3D image quality assessment via analysis of naturalness, structure, and binocular asymmetry | |
CN107396095B (en) | A kind of no reference three-dimensional image quality evaluation method | |
CN105338343A (en) | No-reference stereo image quality evaluation method based on binocular perception | |
Liu et al. | Blind stereoscopic image quality assessment based on hierarchical learning | |
CN108259893B (en) | Virtual reality video quality evaluation method based on double-current convolutional neural network | |
CN109191460A (en) | A kind of quality evaluating method for tone mapping image | |
CN107067388A (en) | A kind of objective evaluation method for quality of stereo images based on GA ELM | |
CN104881684A (en) | Stereo image quality objective evaluate method | |
CN113782190B (en) | Image processing method based on multistage space-time characteristics and mixed attention network | |
CN105049838A (en) | Objective evaluation method for compressing stereoscopic video quality | |
CN109523513A (en) | Based on the sparse stereo image quality evaluation method for rebuilding color fusion image | |
Karimi et al. | Blind stereo quality assessment based on learned features from binocular combined images | |
Tao et al. | Point cloud projection and multi-scale feature fusion network based blind quality assessment for colored point clouds |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
EXSB | Decision made by sipo to initiate substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
WD01 | Invention patent application deemed withdrawn after publication | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20150826 |