CN101345891B - Non-reference picture quality appraisement method based on information entropy and contrast - Google Patents

Non-reference picture quality appraisement method based on information entropy and contrast Download PDF

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CN101345891B
CN101345891B CN2008100701701A CN200810070170A CN101345891B CN 101345891 B CN101345891 B CN 101345891B CN 2008100701701 A CN2008100701701 A CN 2008100701701A CN 200810070170 A CN200810070170 A CN 200810070170A CN 101345891 B CN101345891 B CN 101345891B
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CN101345891A (en
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谢正祥
刘玉红
胡琴
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Chongqing Medical University
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Abstract

The invention discloses a non-reference image quality evaluation method based on information entropy and contrast, comprising the following steps: 1, obtaining an original image; 2, transforming the original image into a gray level image; 3, computing average gray level of the original image and continuous bandwidth of the gray spectrum; 4, determining the search range of the gray initial value Theta and the gray level Delta; 5, performing Zadeh-X transformation to the image according to Theta and Delta values; 6, computing average contrast and information entropy of the transformed image, constructing a non-reference image quality evaluation function by the product of the average contrast and the information entropy, and computing the value thereof; 7, adding 1 to Delta and returning to step 5; 8, computing the maximum of the evaluation function after finishing research as well as corresponding delta and Theta values, performing Zadeh-X transformation to the original image according to the two values and then obtaining the best quality image. The invention provides the criterion of the best image quality, and obtains the best quality image according to the criterion.

Description

Non-reference picture quality appraisement method based on comentropy and contrast
Technical field
The present invention relates to image processing field, specifically, is that a kind of decline type gray scale that has from 0 gray scale that is used to estimate based on comentropy and contrast is composed the non-reference picture method for quality that distributes.
Background technology
The objective evaluation of picture quality is an important very difficult again problem during computer digital image is handled, current a large amount of research and patents all is about the reference picture quality evaluation is arranged, estimate the situation that degrades of image after treatment, non-reference picture quality appraisement (NR-IQA:no reference image quality assessment) then is the most important and the most difficult task that computer digital image is handled, and it has the application of following three aspects: (1) is used for image/video supervisory control system monitoring image quality; (2) be used to adjust image/video treatment system and algorithm; (3) be used for embedded images/processing system for video with optimized algorithm and parameter setting.But present non-reference picture quality appraisement technology is to estimate the improvement of quality behind the image denoising, does not relate to the criterion of optimum picture quality.
Summary of the invention
The object of the present invention is to provide a kind of non-reference picture quality appraisement method, the criterion that obtains optimum picture quality is provided based on comentropy and contrast.
To achieve these goals, technical scheme of the present invention is as follows: a kind of non-reference picture quality appraisement method based on comentropy and contrast, and its key is to carry out as follows:
(1) obtains original image;
Original image is a digital picture, can pass through acquisitions such as camera, video camera, monitor.
(2) original image is converted to original-gray image;
If original image originally as gray level image, does not then need conversion, if original image then needs to be converted into gray level image originally as coloured image, coloured image is converted to gray level image two kinds of methods:
First kind is to adopt normalization weighted sum formula that coloured image is converted to gray level image:
O(x,y)=R(x,y)×0.3+G(x,y)×0.59+B(x,y)×0.11
In the formula, R (x, y), G (x, y), (x y) is respectively pixel (x, three kinds of chromatic values of red, green, blue y) to B.(x is y) for changing the gray value of back corresponding points for O.
Second kind is that power conversion formulas such as employing are changed:
O(x,y)=R(x,y)/3+G(x,y)/3+B(x,y)/3
In the formula, R (x, y), G (x, y), (x y) is respectively pixel (x, three kinds of chromatic values of red, green, blue y) to B.(x is y) for changing the gray value of back corresponding points for O.
(3) (x, y) information are calculated the average gray value of this original-gray image to obtain the gray value O of this original-gray image
Figure B2008100701701D00021
With the gray scale spectrum, and according to this gray scale spectrum acquisition gray scale spectrum continuous bandwidth BW;
Described gray scale spectrum continuous bandwidth is represented one section tonal range of spectral line continuous distribution, begin search from first spectral line of gray scale spectrum, in case do not have spectral line on certain gray scale, promptly no pixel exists on this gray scale, then stop search, the width of this section Continuous Gray Scale spectrum is bandwidth BW.
Described gray scale spectrum is pressed following formula and is obtained:
T ( g ) = O 1 / m ( g ) Σ g = 0 255 O 1 / m ( g ) Σ g = 0 255 O ( g )
In the formula, m is the planarization level, get [1, the ∞) integer between.Correlation theory is seen the Chinese invention patent (patent No.: ZL200610054324.9) that " is used for the gradation of image of bottom layer image mining or the high-resolution detection method of chrominance information ".
(4) determine gray scale initial value Theta and hunting zone; And give tonal range Delta with the starting point assignment of hunting zone;
(5) according to the gray value O of described gray scale initial value Theta, tonal range Delta and original-gray image (x y), carries out conversion with the Zadeh-X transform method to original-gray image, obtain after one group of conversion the T of grey scale pixel value as a result (x, y);
Described Zadeh-X transform method adopts following formula to calculate:
T ( x , y ) = K O ( x , y ) - Theta Delta
Wherein, (x y) is the coordinate of each pixel in the image, O (x, y) scope is [0,255], and T (x, scope y) is [0,255], Theta is an integer, and span is [0,255], Delta is a positive integer, and span is [1,255], K represents contraction-expansion factor, and span is [1,255], be more evenly distributed, generally get K=255 in order to make the gradation of image after the conversion.
When O (x, y)<during Theta, T (x, y)=0; When O (x, in the time of y)>(Theta+Delta), T (x, y)=255.Correlation theory see Chinese invention patent " bottom layer image hide and method for digging and adopt the image concealing and the excavating gear of this method " (the patent No.: ZL200610054379.X).
(6) (x y), calculates this average contrast of grey scale pixel value correspondence as a result according to the T of grey scale pixel value as a result after the conversion that obtains With comentropy InEn;
(7) according to the average contrast
Figure B2008100701701D00033
With comentropy InEn, obtain the value IQAF of evaluation function, described evaluation function is:
IQAF = C ‾ × InEn
(8) Delta is added 1, turn back to step (five) again, terminal point up to the hunting zone;
(9) after search finishes, find out the maximum IQAF and the Delta value of this IQAF correspondence, original-gray image is carried out conversion, obtain the T of the grey scale pixel value as a result (x after one group of conversion with the Zadeh-X transform method, y), the image of this value formation is best quality image.
In other words, the criterion of optimum picture quality is the maximum of IQAF, the Delta value corresponding according to maximum IQAF value, and (x y), just can obtain the image of best in quality with the Zadeh-X transform method in conjunction with the gray value O of Theta and original image again.
In described step (four), described gray scale initial value Theta is the gray scale spectrum starting point gray value of band continuously, and the starting point of hunting zone is the average gray value of original-gray image
Figure B2008100701701D00041
Terminal point is
Figure B2008100701701D00042
The present invention is used to assess the image that has from the decline type gray scale spectrum distribution of 0 gray scale, so Theta generally gets 0 value.And find that in actual search maximum generally exists
Figure B2008100701701D00043
The preceding appearance is so the terminal point of hunting zone is made as
Figure B2008100701701D00044
Can save search time.
The average gray value of described gray level image Obtain by following formula:
G ‾ = 1 M × N Σ N - 1 Σ M - 1 Gray ( x , y )
In the formula, (x is that (M, N are the pixel count of x, y direction to pixel for x, gray scale y) y) to Gray.Because Delta is a positive integer, if therefore calculate average gray value
Figure B2008100701701D00047
With
Figure B2008100701701D00049
Be decimal, then adopt the mode round up, they are adjusted into integer after assignment give Delta.
In described step (six), described average contrast Obtain by following formula:
C ‾ = 1 ( M - 1 ) × ( N - 1 ) Σ N - 2 Σ M - 2 | Gray ( x , y ) - Gray ( x + 1 , y ) |
In the formula, (x is that (M, N are the pixel count of x, y direction to pixel for x, gray scale y) y) to Gray.
In described step (six), described comentropy InEn is obtained by following formula:
InEn = - Σ 255 p ( i ) Log 2 p ( i )
In the formula, the probability of the pixel of p (i) expression gray scale i.
Beneficial effect: the present invention proposes a kind of non-reference picture quality appraisement method based on comentropy and contrast, be used to estimate and have the image that distributes from the decline type gray scale spectrum of 0 gray scale, adopting the product of comentropy and contrast is evaluation function, and search for this evaluation function maximum, the image of the corresponding best in quality of this maximum, the criterion of optimum picture quality is provided,, has obtained the image of best in quality according to this evaluation function maximum corresponding parameters.
Description of drawings
Fig. 1 is a workflow schematic diagram of the present invention;
Fig. 2 is an original image;
Fig. 3 is the picture quality comparison diagram, a) is original image shown in Figure 2 wherein, b) is the image of IQAF maximum correspondence, c) is the corresponding image of another IQAF value.
Embodiment
Further the present invention is illustrated below in conjunction with drawings and Examples.
As shown in Figure 1: a kind of non-reference picture quality appraisement method based on comentropy and contrast, carry out as follows:
(1) obtains original image;
Original image is a digital picture, can pass through acquisitions such as camera, video camera, monitor.As shown in Figure 2: the width of cloth gray scale pictures of original image under low lighting condition, taking.
(2) original image is converted to original-gray image;
As shown in Figure 2, original image does not need to change originally as gray level image.If but original image is a coloured image, then needs to be converted into gray level image, coloured image is converted to gray level image two kinds of methods:
First kind is to adopt normalization weighted sum formula to change:
O(x,y)=R(x,y)×0.3+G(x,y)×0.59+B(x,y)×0.11
In the formula, R (x, y), G (x, y), (x y) is respectively pixel (x, three kinds of chromatic values of red, green, blue y) to B.(x is y) for changing the gray value of back corresponding points for O.
Second kind is that power conversion formulas such as employing are changed:
O(x,y)=R(x,y)/3+G(x,y)/3+B(x,y)/3
In the formula, R (x, y), G (x, y), (x y) is respectively pixel (x, three kinds of chromatic values of red, green, blue y) to B.(x is y) for changing the gray value of back corresponding points for O.
(3) (x, y) information are calculated the average gray value of this original-gray image to obtain the gray value O of this original-gray image With the gray scale spectrum, and according to this gray scale spectrum acquisition gray scale spectrum continuous bandwidth BW;
The average gray value of described gray level image Obtain by following formula:
G ‾ = 1 M × N Σ N - 1 Σ M - 1 Gray ( x , y )
In the formula, (x is that (M, N are the pixel count of x, y direction to pixel for x, gray scale y) y) to Gray.The average gray value of image shown in Fig. 2
Figure B2008100701701D00064
Being 4.7758, is 5 after rounding up.
Described gray scale spectrum continuous bandwidth is represented one section tonal range of spectral line continuous distribution, begin search from first spectral line of gray scale spectrum, in case do not have spectral line on certain gray scale, promptly no pixel exists on this gray scale, then stop search, the width of this section Continuous Gray Scale is bandwidth BW.
Described gray scale spectrum is pressed following formula and is obtained:
T ( g ) = O 1 / m ( g ) Σ g = 0 255 O 1 / m ( g ) Σ g = 0 255 O ( g )
In the formula, m is the planarization level, get [1, the ∞) integer between.Correlation theory is seen the Chinese invention patent (patent No.: ZL200610054324.9) that " is used for the gradation of image of bottom layer image mining or the high-resolution detection method of chrominance information ".These gray scale spectrum computational methods have the precision of a pixel, calculate the continuous bandwidth BW=66 of gradation of image spectrum among Fig. 2 according to following formula.
(4) determine the hunting zone of gray scale initial value Theta and Delta; Described gray scale initial value Theta is the gray scale spectrum starting point gray value of band continuously, and the starting point of Delta hunting zone is the average gray value of original-gray image
Figure B2008100701701D00066
Terminal point is
Figure B2008100701701D00067
The present invention is used to estimate the image that has from the decline type gray scale spectrum distribution of 0 gray scale, so Theta generally gets 0 value.And find that in actual search maximum generally exists
Figure B2008100701701D00068
The preceding appearance is so the terminal point of hunting zone is made as
Figure B2008100701701D00069
Can save search time.
Figure B2008100701701D000610
In the present embodiment, the starting point of hunting zone is 5, and terminal point is 13.
(5) according to the gray value O of described gray scale initial value Theta, tonal range Delta and original-gray image (x y), carries out conversion with the Zadeh-X transform method to original-gray image, obtain after one group of conversion the T of grey scale pixel value as a result (x, y);
Described Zadeh-X transform method adopts following formula to calculate:
T ( x , y ) = K O ( x , y ) - Theta Delta
Wherein, (x y) is the coordinate of each pixel in the image, O (x, scope y) is [0,255], T (x, scope y) is [0,255], Theta ∈ [0,255], Delta ∈ [1,255], K represents contraction-expansion factor, span be [, 255], be more evenly distributed, generally get K=255 in order to make the gradation of image after the conversion.
When O (x, y)<during Theta, T (x, y)=0; When O (x, in the time of y)>(Theta+Delta), T (x, y)=255.
(6) (x y), calculates this average contrast of grey scale pixel value correspondence as a result according to the T of grey scale pixel value as a result after the conversion that obtains
Figure B2008100701701D00072
With comentropy InEn;
Described average contrast
Figure B2008100701701D00073
Obtain by following formula:
C ‾ = 1 ( M - 1 ) × ( N - 1 ) Σ N - 2 Σ M - 2 | Gray ( x , y ) - Gray ( x + 1 , y ) |
In the formula, (x is that (M, N are the pixel count of x, y direction to pixel for x, gray scale y) y) to Gray.
Described comentropy InEn is obtained by following formula:
InEn = - Σ 255 p ( i ) Log 2 p ( i )
(7) according to the average contrast With comentropy InEn, obtain the value IQAF of evaluation function, described evaluation function is:
IQAF = C ‾ × InEn
(8) tonal range Delta is added 1, turn back to step (five) again, terminal point up to the hunting zone;
(9) searched for after, find out the maximum IQAF and the Delta value of this IQAF correspondence, with the Zadeh-X transform method original-gray image is carried out conversion, obtain the T of the grey scale pixel value as a result (x after one group of conversion, y), (x, y) image of Gou Chenging is best quality image to this grey scale pixel value T.
Following table has shown the hunting zone according to delta, searches for the evaluation function value that obtains one by one:
Figure B2008100701701D00081
As shown in Figure 3: among the figure, a) be original image shown in Figure 2, the image of correspondence when b) being Delta=8, the image of correspondence when c) being Delta=2 for the IQAF maximum.We as can be seen, picture quality the best of IQAF maximum correspondence, and the average gray value of this figure
Figure B2008100701701D00082
Near 128.

Claims (5)

1. non-reference picture quality appraisement method based on comentropy and contrast is characterized in that carrying out as follows:
(1) obtains original image;
(2) original image is converted to original-gray image;
(3) (x, y) information are calculated the average gray value of this original-gray image to obtain the gray value O of this original-gray image
Figure F2008100701701C00011
With the gray scale spectrum, and according to this gray scale spectrum acquisition gray scale spectrum continuous bandwidth BW;
(4) determine the hunting zone of gray scale initial value Theta and gray-level Delta;
(5) according to the gray value O of described gray scale initial value Theta, gray-level Delta and original-gray image (x y), carries out conversion with the Zadeh-X transform method to original-gray image, obtain after one group of conversion the T of grey scale pixel value as a result (x, y);
(6) (x y), calculates this average contrast of grey scale pixel value correspondence as a result according to the T of grey scale pixel value as a result after the conversion that obtains
Figure F2008100701701C00012
With comentropy InEn;
(7) according to the average contrast
Figure F2008100701701C00013
With comentropy InEn, obtain the value IQAF of evaluation function, described evaluation function is:
IQAF = C ‾ × InEn
(8) gray-level Delta is added 1, turn back to step (five) again, up to the search terminal point;
(9) after search finishes, find out the maximum IQAF and the Delta value of this IQAF correspondence, with the Zadeh-X transform method original-gray image is carried out conversion, obtain the T of the grey scale pixel value as a result (x after one group of conversion, y), this as a result grey scale pixel value T (x, y) image of Gou Chenging is best quality image.
2. according to the described non-reference picture quality appraisement method of claim 1 based on comentropy and contrast, it is characterized in that: in described step (four), described gray scale initial value Theta is the gray scale spectrum starting point gray value of band continuously, and the starting point of gray-level Delta hunting zone is the average gray value of original-gray image
Figure F2008100701701C00015
Terminal point is
Figure F2008100701701C00016
3. according to the described non-reference picture quality appraisement method of claim 2, it is characterized in that: the average gray value of described gray level image based on comentropy and contrast
Figure F2008100701701C00021
Obtain by following formula:
G ‾ = 1 M × N Σ N - 1 Σ M - 1 Gray ( x , y )
In the formula, (x is that (M, N are the pixel count of x, y direction to pixel for x, gray scale y) y) to Gray.
4. according to the described non-reference picture quality appraisement method of claim 1, it is characterized in that based on comentropy and contrast: in described step (six), described average contrast
Figure F2008100701701C00023
Obtain by following formula:
C ‾ = 1 ( M - 1 ) × ( N - 1 ) Σ N - 2 Σ M - 2 | Gray ( x , y ) - Gray ( x + 1 , y ) |
In the formula, (x is that (M, N are the pixel count of x, y direction to pixel for x, gray scale y) y) to Gray.
5. according to the described non-reference picture quality appraisement method based on comentropy and contrast of claim 1, it is characterized in that: in described step (six), described comentropy InEn is obtained by following formula:
InEn = - Σ 255 p ( i ) Log 2 p ( i )
In the formula, gray scale is the probability of the pixel of i in p (i) the expression gray scale spectrum.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103475898A (en) * 2013-09-16 2013-12-25 北京理工大学 Non-reference image quality assessment method based on information entropy characters

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101620729B (en) * 2009-07-31 2011-11-30 重庆医科大学 Method for producing gray image with best quality
CN101964109B (en) * 2010-10-15 2012-08-15 重庆医科大学 Automatic adaptive acquisition method of best quality image in bottom image mining
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5481376A (en) * 1993-06-23 1996-01-02 Toyo Boseki Kabushiki Kaisha Method and apparatus for improving image quality
WO2004049243A1 (en) * 2002-11-25 2004-06-10 Sarnoff Corporation Method and apparatus for measuring quality of compressed video sequences without references
US20060017678A1 (en) * 2004-07-20 2006-01-26 Makoto Shiomi Driver device for liquid crystal display, computer program and storage medium, and liquid crystal display
CN1885317A (en) * 2006-07-06 2006-12-27 上海交通大学 Adaptive edge detection method based on morphology and information entropy
CN1885954A (en) * 2005-06-23 2006-12-27 华为技术有限公司 Blocking effect measuring method and video quality estimation method
CN101087438A (en) * 2006-06-06 2007-12-12 安捷伦科技有限公司 System and method for computing packet loss measurement of video quality evaluation without reference

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5481376A (en) * 1993-06-23 1996-01-02 Toyo Boseki Kabushiki Kaisha Method and apparatus for improving image quality
WO2004049243A1 (en) * 2002-11-25 2004-06-10 Sarnoff Corporation Method and apparatus for measuring quality of compressed video sequences without references
US20060017678A1 (en) * 2004-07-20 2006-01-26 Makoto Shiomi Driver device for liquid crystal display, computer program and storage medium, and liquid crystal display
CN1885954A (en) * 2005-06-23 2006-12-27 华为技术有限公司 Blocking effect measuring method and video quality estimation method
CN101087438A (en) * 2006-06-06 2007-12-12 安捷伦科技有限公司 System and method for computing packet loss measurement of video quality evaluation without reference
CN1885317A (en) * 2006-07-06 2006-12-27 上海交通大学 Adaptive edge detection method based on morphology and information entropy

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
US 5481376 A,全文.

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103475898A (en) * 2013-09-16 2013-12-25 北京理工大学 Non-reference image quality assessment method based on information entropy characters
CN103475898B (en) * 2013-09-16 2015-05-20 北京理工大学 Non-reference image quality assessment method based on information entropy characters

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