CN101950422A - Singular value decomposition(SVD)-based image quality evaluation method - Google Patents
Singular value decomposition(SVD)-based image quality evaluation method Download PDFInfo
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Abstract
The invention discloses a singular value decomposition(SVD)-based image quality evaluation method, which comprises the following five steps of: 1, respectively dividing an original image and a distorted image with the size of m*n into m*n/k*k image blocks; 2, programming all the image blocks according to an SVD principle so as to implement SVD; 3, calculating structural distortion in left and right singular vectors U and V and U(p) and V(p) of the image blocks of the original image and the distorted image, and simultaneously calculating brightness distortion in singular value characteristic vectors S and S(p) of the image blocks of the original image and the distorted image; 4, integrating the brightness distortion and structural distortion of the image blocks of the original image and the distorted image to obtain a formula D1, performing cycle calculation to obtain D1 values of all m*n/k*k image blocks, and solving a mean value; and 5, integrating the image quality evaluation parameters, defining an SVD-based image quality evaluation index, wherein when a QSVD value is zero, the image quality is the best, the QSVD value is increased with the change of QSVD, and the image quality is worse. The SVD-based image quality evaluation method has the advantages of practical value and wide application prospect in the field of image quality evaluation.
Description
Technical field
The present invention proposes a kind of image quality evaluating method based on svd, belongs to the image quality evaluation field.Be specifically related to a kind ofly, obtain to contain the objective image quality evaluating method of the singularity characteristics vector of picture structure information and monochrome information based on the image svd.
Background technology
Image quality evaluation is one of gordian technique of image processing system, has several factors to have influence on judgement to the picture quality quality.The method of image quality evaluation and index, can encode at video, in the application such as image processing system to how improving picture quality, selecting corresponding image processing method that useful guide is provided.
Image quality evaluating method mainly is divided into subjective quality evaluation method and method for evaluating objective quality two classes.Subjective evaluation method is meant that the quality that a plurality of observers are treated the evaluation map picture carries out subjective marking and be weighted average comprehensive evaluation, the most frequently used subjective quality evaluation method has subjective average point-score (Mean Opinion Score, MOS), the subjective average point-score of difference (Difference Mean Opinion Score, DMOS) etc.Because of needing numerous evaluators in actual applications, it participates in, step complexity, length consuming time, expense height, and the result is subject to the influence of observer, test condition and environment, and poor stabilities etc. are very limited.
Present research is based on method for objectively evaluating, and goal in research is to make Environmental Evaluation Model reflect the subjective quality of human eye vision perception exactly.The fundamental purpose of evaluating objective quality is to express the subjective feeling of people to image with quantizating index objective, that quantitative mathematical model provides or parameter.The application of mathematical model makes the characteristics that the evaluating objective quality of image has fast, stablizes, is easy to be quantized.
At present the most frequently used objective image quality evaluation index be square error (Mean Square Error, MSE) and Y-PSNR (Peak Signal Noise Ratio, PSNR), they all are based on the objective image quality evaluating method of statistical property.The calculating of MSE and PSNR is relatively more directly perceived, simple, and this makes them be used widely always.But only be to the pure mathematics of error between pixel statistics, do not consider that (Human Visual System, apperceive characteristic HVS) do not meet people's subjective feeling under many circumstances for correlativity between pixel and human visual system.
Based on the HVS model, a kind of image quality evaluation new method is proposed on the basis of analyzing the main visual characteristic of human eye, the treatment step of these methods comprises: pre-service, CSF filtering, passage decomposition, error quantization and error merging etc.But some intrinsic difficulties have hindered the development of these class methods.At first, because human-eye visual characteristic is very complicated, known several human-eye visual characteristics have only been considered in present research, and people are also not thorough to the understanding of HVS, can't set up accurate, unified model, and this directly has influence on the accuracy that quality is estimated.Secondly, HVS is a very complicated system, and the simulation that HVS is formed structure can cause algorithm complexity, operand big usually.Therefore, can't replace simple error criterions such as the PSNR that is widely adopted and MSE at present based on the method for HVS model.
Scholars such as Zhou Wang have proposed a kind of image quality evaluating method based on structure distortion, structural similarity theoretical and structural similarity (Structural Similarity, SSIM) index.The structural similarity theory thinks that from the allomeric function of high-level simulation HVS the major function of HVS is to extract structural information from the visual field, therefore uses tolerance being similar to as the image perceived quality to structural information.Angle that this image quality evaluating method is formed from image is defined as structural information and is independent of brightness, contrast, the attribute of object structures in the reflection scene, and be that the combination of brightness, three different factors of contrast and structure is analyzed and researched with distortion modeling, obtained better image quality assessment effect based on the structure distortion model.Equally, also there is certain defective in this method.For example, the accuracy for the image quality evaluation of bluring type of distortion is lower; Evaluation to different type of distortion, different strength of distortion images is lack of consistency.
Also there is the scholar to attempt utilizing the computing method of svd, sums up the method that is used for image quality evaluation.For example, the gloomy people of grade that holds high up proposes the quality evaluating method based on svd, utilize the character of the angle between the different singular value features vectors that picture quality is estimated, this method is lack of consistency to the evaluation of different type of distortion, different strength of distortion images.Method is estimated in a kind of svd compressed image quality assessment based on visual weight that people such as Zhang Fei swallow propose, and is primarily aimed at the compression reconstructed image and carries out quality assessment.
Summary of the invention
1, purpose: the purpose of this invention is to provide a kind of image quality evaluating method based on svd, this method is on the basis of image block, based on svd, propose in conjunction with decomposing the left singular vector U that the back obtains, right singular vector V and singular value features vector S, contain picture structure information and monochrome information in these singularity characteristics vectors, calculate, sum up a kind of image quality evaluating method based on svd according to specific method.
2, technical scheme: a kind of image quality evaluating method of the present invention based on svd, these method concrete steps are as follows:
Step 1: with original image and distorted image, size is m * n, is divided into the image block of rule, for example, and k * k piecemeal.The image block number is (m * n/k * k) individual.(k is less than m, n)
Step 2: set by step one carry out piecemeal after, the image block that original image and distorted image are divided into is realized svd according to the svd principle programming of numerical evaluation, obtains to divide a solution vector, U, V, S and U
(p), V
(p), S
(p)Wherein, the left singular vector that on behalf of original image single image piece svd, U, V, S obtain respectively, right singular vector and singular value features vector; U
(p), V
(p), S
(p)Represent the left singular vector of distorted image correspondence image piece svd respectively, right singular vector and singular value features vector.
Step 3:, calculate left and right singular vector U, V and the U of original image single image piece and distorted image correspondence image piece according to the branch solution vector that obtains in the step 2
(p), V
(p)In contained picture structure information have structure distortion, therefore consider construct image structure distortion C
UV, specific as follows:
Left side singular vector size is k * k, represents U by the column vector mode, U=[U
1, U
2... U
i]
1 * kRight singular value vector size is k * k, represents V by the column vector mode, V=[V
1, V
2... V
i]
1 * k
Structure
And make γ
i=(α
i+ β
i),
Original image single image piece and distorted image correspondence image piece branch solution vector U, V and U have been represented
(p), V
(p)In the full detail that contains.
Structure
Represented the image block branch solution vector U of original image and undistorted image, V and U, the full detail that contains among the V.Know by the svd feature,
Think γ
0iAnd γ
iDifference presentation video information loss.The structure computing method
Finish the picture structure distortion of original image single image piece and distorted image correspondence image piece is weighed.
Simultaneously, according to the branch solution vector that obtains in the step 2, calculate the singular value features vector S and the S of original image single image piece and distorted image correspondence image piece
(p)In contained image luminance information have luminance distortion, so construct image luminance distortion C
S, specific as follows:
Original image single image piece svd obtains singular value features vector S=[σ
1, σ
1..., σ
i], corresponding distorted image correspondence image piece svd obtains the singular value features vector
The structure computing method
Finish the brightness of image distortion of original image single image piece and distorted image correspondence image piece is weighed.
Step 4: the picture structure distortion and the brightness of image distortion of combined calculation original image single image piece and distorted image correspondence image piece obtain computing formula D
l, D wherein
l=C
UVC
SD
lInformation loss in expression original image single image piece and the distorted image correspondence image piece.For m * n/k * k image block, cycle calculations obtains m * n/k * k D
lValue.And ask for the average of all images piece
Step 5: comprehensive above-mentioned image quality evaluation parameter, definition is based on the image quality evaluation index of svd
When the QSVD value was 0, then picture quality was best, and along with QSVD changes, the QSVD value is big more, and then picture quality is poor more.
3, advantage and effect:
With image block, and the image block and the distorted image correspondence image piece of the original image behind the regular piecemeal carried out svd based on the image quality evaluating method of svd.On this basis, in conjunction with the U that produces after the svd, V, S comprise picture structure information and image luminance information, and the principal element that has realized considering influencing picture quality is the method that brightness and structure distortion carry out image quality evaluation.Experimental result shows, more can be consistent when the objective evaluation image based on the image quality evaluating method of svd with the human eye subjective sensation, have effect preferably.And because selected calculation relational expression is comparatively simple, the part statistical characteristic value that only relates to original image and distorted image calculates, and need not original pixel scale information, and data volume is less relatively, therefore, this method can be estimated picture quality fast and effectively.
Description of drawings
Fig. 1 is a kind of image quality evaluating method process blocks synoptic diagram based on svd of the present invention
Symbol description is as follows among the figure:
In the step 2,
Expression is carried out svd by the svd numerical computation method to image block.
In the step 3, C
UVThe picture structure distortion of expression original image single image piece and distorted image correspondence image piece.C
SRepresent the brightness of image distortion of original image single image piece and distorted image correspondence image piece.
In the step 4, D
l=C
UVC
SThe picture structure distortion and the brightness of image distortion of expression original image single image piece and distorted image correspondence image piece.
In the step 5,
It is the evaluation method final calculation result that the present invention proposes based on the picture quality of svd.
Embodiment
The present invention is a kind of image quality evaluating method based on svd, and it may further comprise the steps:
Software reads in original image and distorted image, is converted to gray-scale map, utilizes the inventive method to handle image, as shown in Figure 1, can obtain image quality evaluation values.Select the image that JPEG compressed format is handled in the LIVE IMAGE DATABASE database for use.The image of choosing is lighthouse2.bmp.
Step 1: at first programming is divided into regular image block with original image and distorted image.The image size is 768 * 512, and the size of choosing piecemeal in this experiment is 8 * 8, and the image block number is 96 * 64, as shown in Figure 1, and i=96, j=64.
Step 2: set by step one carry out piecemeal after, to all images piece that original image and distorted image are divided into, utilize software programming to realize the svd mathematical computations, obtain the svd vector, U, V, S and U
(p), V
(p), S
(p)Wherein, the left singular vector that on behalf of original image single image piece svd, U, V, S obtain, right singular vector and singular value features vector; U
(p), V
(p), S
(p)Represent the left singular vector of distorted image correspondence image piece svd, right singular vector and singular value features vector.
Step 3:, calculate left and right singular vector U, V and the U of original image single image piece and distorted image correspondence image piece according to the svd vector that obtains in the step 2
(p), V
(p)In contained picture structure information have structure distortion, so construct image structure distortion C
UV, computing method are
Simultaneously, calculate the singular value features vector S and the S of original image single image piece and distorted image correspondence image piece
(p)In contained image luminance information have luminance distortion, construct image luminance distortion C
S, computing method are
Step 4: the picture structure distortion and the brightness of image distortion of combined calculation original image single image piece and distorted image correspondence image piece obtain computing formula D
l, D wherein
l=C
UVC
SD
lInformation loss in expression original image single image piece and the distorted image correspondence image piece.For the image block and the distorted image correspondence image piece of 96 * 64 original images,, cycle calculations obtains 96 * 64 D according to the method described above
lValue.And after asking for k * k piecemeal, the average of all images piece
Step 5:, calculate respectively and obtain D for the image block and the distorted image correspondence image piece of whole 96 * 64 original images
lValue and average
A kind of image quality evaluation index according to the present invention's definition based on svd
Calculate, obtain image quality evaluation objective value result.
Finally, this method calculates JPEG compression artefacts format-pattern quality assessment value.
Claims (1)
1. image quality evaluating method based on svd, it is characterized in that: these method concrete steps are as follows:
Step 1: with original image and distorted image, size is m * n, is divided into the image block k * k piecemeal of rule, and the image block number is m * n/k * k, and k is less than m, n;
Step 2: set by step one carry out piecemeal after, the image block that original image and distorted image are divided into is realized svd according to the svd principle programming of numerical evaluation, obtains to divide a solution vector, U, V, S and U
(p), V
(p), S
(p)Wherein, the left singular vector that on behalf of original image single image piece svd, U, V, S obtain respectively, right singular vector and singular value features vector; U
(p), V
(p), S
(p)Represent the left singular vector of distorted image correspondence image piece svd respectively, right singular vector and singular value features vector;
Step 3:, calculate left and right singular vector U, V and the U of original image single image piece and distorted image correspondence image piece according to the branch solution vector that obtains in the step 2
(p), V
(p)In contained picture structure information have structure distortion, therefore consider construct image structure distortion C
UV, be implemented as follows:
Left side singular vector size is k * k, represents U by the column vector mode, U=[U
1, U
2... U
i]
1 * kRight singular value vector size is k * k, represents V by the column vector mode, V=[V
1, V
2... V
i]
1 * k
Structure
And make γ
i=(α
i+ β
i),
Original image single image piece and distorted image correspondence image piece branch solution vector U, V and U have been represented
(p), V
(p)In the full detail that contains;
Structure
Represented the image block branch solution vector U of original image and undistorted image, V and U, the full detail that contains among the V; Know by the svd feature,
Think γ
0iAnd γ
iDifference presentation video information loss, the structure computing method
Finish the picture structure distortion of original image single image piece and distorted image correspondence image piece is weighed;
Simultaneously, calculate the singular value features vector S and the S of original image single image piece and distorted image correspondence image piece
(p)In contained image luminance information have luminance distortion, so construct image luminance distortion C
S, be implemented as follows:
Original image single image piece svd obtains singular value features vector S=[σ
1, σ
2..., σ
i], corresponding distorted image correspondence image piece svd obtains the singular value features vector
The structure computing method
Finish the brightness of image distortion of original image single image piece and distorted image correspondence image piece is weighed;
Step 4: the picture structure distortion and the brightness of image distortion of combined calculation original image single image piece and distorted image correspondence image piece obtain computing formula D
l, D wherein
l=C
UVC
SD
lInformation loss in expression original image single image piece and the distorted image correspondence image piece; For m * n/k * k image block, cycle calculations obtains m * n/k * k D
lBe worth, and ask for the average of all images piece
Step 5: comprehensive above-mentioned image quality evaluation parameter, definition is based on the image quality evaluation index of svd
When the QSVD value was 0, then picture quality was best, and along with QSVD changes, the QSVD value is big more, and then picture quality is poor more.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102209257A (en) * | 2011-06-17 | 2011-10-05 | 宁波大学 | Stereo image quality objective evaluation method |
| CN102271279A (en) * | 2011-07-22 | 2011-12-07 | 宁波大学 | Objective analysis method for just noticeable change step length of stereo images |
| CN102436655A (en) * | 2011-09-02 | 2012-05-02 | 清华大学 | Super-resolution reconstruction image quality evaluation method based on SVD (singular value decomposition) |
| CN102595185A (en) * | 2012-02-27 | 2012-07-18 | 宁波大学 | Stereo image quality objective evaluation method |
| CN104103065A (en) * | 2013-04-10 | 2014-10-15 | 无锡金帆钻凿设备股份有限公司 | No-reference fuzzy image quality evaluation method based on singular value decomposition |
| CN104182962A (en) * | 2013-05-28 | 2014-12-03 | 腾讯科技（深圳）有限公司 | Picture definition evaluation method and device |
| CN106600597A (en) * | 2016-12-22 | 2017-04-26 | 华中科技大学 | Non-reference color image quality evaluation method based on local binary pattern |
| CN106650696A (en) * | 2016-12-30 | 2017-05-10 | 山东大学 | Handwritten electrical element identification method based on singular value decomposition |
| CN107085834A (en) * | 2017-04-25 | 2017-08-22 | 西安工程大学 | A kind of image de-noising method based on image rotation and piecemeal singular value decomposition |
| CN108510496A (en) * | 2018-04-09 | 2018-09-07 | 杭州电子科技大学 | The fuzzy detection method that SVD based on Image DCT Domain is decomposed |
| CN109544491A (en) * | 2018-10-17 | 2019-03-29 | 天津大学 | Screen picture quality evaluating method based on gradient singular value decomposition |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1897634A (en) * | 2006-06-08 | 2007-01-17 | 复旦大学 | Image-quality estimation based on supercomplex singular-value decomposition |
| JP2007280273A (en) * | 2006-04-11 | 2007-10-25 | Fuji Xerox Co Ltd | Image evaluation apparatus, image evaluation method and program |
| CN101441768A (en) * | 2008-11-28 | 2009-05-27 | 武汉大学 | Image quality evaluating method based on structure distortion and image definition |
-
2010
- 2010-09-29 CN CN201010296639.0A patent/CN101950422B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007280273A (en) * | 2006-04-11 | 2007-10-25 | Fuji Xerox Co Ltd | Image evaluation apparatus, image evaluation method and program |
| CN1897634A (en) * | 2006-06-08 | 2007-01-17 | 复旦大学 | Image-quality estimation based on supercomplex singular-value decomposition |
| CN101441768A (en) * | 2008-11-28 | 2009-05-27 | 武汉大学 | Image quality evaluating method based on structure distortion and image definition |
Non-Patent Citations (1)
| Title |
|---|
| 张飞艳: "基于视觉加权的奇异值分解压缩图像质量评价测度", 《电子与信息学报》 * |
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| CN102209257B (en) * | 2011-06-17 | 2013-11-20 | 宁波大学 | Stereo image quality objective evaluation method |
| CN102271279A (en) * | 2011-07-22 | 2011-12-07 | 宁波大学 | Objective analysis method for just noticeable change step length of stereo images |
| CN102271279B (en) * | 2011-07-22 | 2013-09-11 | 宁波大学 | Objective analysis method for just noticeable change step length of stereo images |
| CN102436655A (en) * | 2011-09-02 | 2012-05-02 | 清华大学 | Super-resolution reconstruction image quality evaluation method based on SVD (singular value decomposition) |
| CN102436655B (en) * | 2011-09-02 | 2014-01-15 | 清华大学 | Super-resolution reconstruction image quality evaluation method based on SVD (singular value decomposition) |
| CN102595185A (en) * | 2012-02-27 | 2012-07-18 | 宁波大学 | Stereo image quality objective evaluation method |
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| CN106600597B (en) * | 2016-12-22 | 2019-04-12 | 华中科技大学 | It is a kind of based on local binary patterns without reference color image quality evaluation method |
| CN106650696A (en) * | 2016-12-30 | 2017-05-10 | 山东大学 | Handwritten electrical element identification method based on singular value decomposition |
| CN106650696B (en) * | 2016-12-30 | 2019-12-10 | 山东大学 | method for identifying handwritten electrical element symbol based on singular value decomposition |
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