CN102147912A - Adaptive difference expansion-based reversible image watermarking method - Google Patents

Abstract

The invention discloses an adaptive difference expansion-based reversible image watermarking method, which comprises a watermark embedding process, a watermark extraction process and an image recovery process; before watermarks are embedded, the original image needs to be pretreated, and the pretreatment comprises alternated division, difference calculation, image complexity calculation and parameter setting; in the embedding process, the corresponding watermarks are embedded by using adaptive expansion; additional information is embedded, and the watermark data and the additional information are mixed and embedded under a specific condition; in the extraction process, the additional information is first extracted, and the difference is calculated; the inverse operation of the adaptive difference expansion is performed, the watermark data are extracted, and the original difference is reduced; and finally, the image is reduced through the reduced difference. The method has reversibility, and can provide higher embedding capacity and better image quality, particularly under the condition of many embedded watermark data.

Description

A kind of reversible image watermark method based on the expansion of self-adaptation difference

Technical field

The present invention relates to a kind of embedding and extracting method of reversible image watermark, particularly a kind of reversible image watermark method based on the expansion of self-adaptation difference.

Background technology

The numerical information revolution has brought deep variation for human society and life, and various Digital Medias works have brought new challenge when enriching people's lives.The digital multimedia works duplicate and distribute the cheap of cost, make digital piracy very general, so safety problems such as digital copyright protecting and content integrity checking become the problem that presses for solution.Digital watermarking in digital picture, text, video or sound signal, has become a kind of effective means of protecting digital media content safety with some Information hiding.In most of existing digital watermark methods, host's medium are because the embedding of watermark signal can and can't revert to embed watermark state before by permanent change.Though the distortion that watermark is introduced is not easy to be discovered by people's sensory perceptual system usually, but the data fidelity is required in the high special dimension at some, as military image, medical image, satellite remote sensing images or legal argument image etc., any small distortion is not allowed to.Therefore the application of digital watermark technology in these fields has been subjected to very big restriction.In order to address this problem, people have proposed the notion of reversible digital watermarking, and this watermarking project can guarantee the virgin state when extracting end host's medium are accurately reverted to it and are not embedded into watermark in embed watermark information protection digital publishing rights.Since Barton in 1997 proposed the notion of reversible water mark for the first time, existing in recent years scholar proposed some reversible water mark algorithms.Existing reversible water mark algorithm consists essentially of dual mode, promptly embeds in the spatial domain and embeds in frequency domain.

Embed reversible water mark in the spatial domain, because realization is simple relatively, the embedding volume ratio is bigger, thereby becomes the focus of nearest research.Invisible reversible water mark in the spatial domain mainly is divided three classes: based on compression of images, based on the difference expansion with based on histogram modification.

Generally adopt lossless compression algorithm that image is compressed with vacating space in the insensitive part of human eye based on the algorithm of compression of images and come embed watermark, the embedding capacity depends on compressibility, the embedding capacity is generally little, and the complexity of compression of images computing efficiently, so this class watermark computation complexity is than higher; Reversible image algorithm based on histogram modification is a statistical property of utilizing image pixel, carries out histogram and moves and obtain embedded space; Algorithm based on the difference expansion is to utilize picture material to have correlativity, neighbor has more close value usually, therefore the difference of two neighbors is less, by the difference of extending neighboring pixel, two data embedding wherein and not can be caused tangible distortion.

Based on difference expansion (Difference Expansion, DE) reversible image watermark, the earliest by Tian propose for the first time 2003 (referring to J.Tian.Reversible data embedding using a difference expansion[J] .IEEE Trans.Circuits Systems and Video Technology.2003,13 (8): 890-896).The difference expansion also can be regarded as a kind of integer wavelet transformation, the HFS of its expansion wavelet transformation, and with the watermark information embedding wherein.DE is also sometimes referred to as the displacement expansion, because the process of its embed watermark can be regarded the process of displacement as.The binary form of supposing difference d is shown (d _N-1d _N-2... d ₁d ₀) ₂, then the binary form of the difference d ' behind the embed watermark is shown (d _N-1d _N-2... d ₁d ₀B) ₂This is equivalent to d then watermark b to be embedded in the lowest order of vacating to one of left dislocation.Alattar (A.M.Alattar.Reversible watermark using difference expansion oftriplets[C] .Proc.IEEE ICIP.2003:501-504; A.M.Alattar.Reversible watermarkusing difference expansion of quads[C] .Proc.ICASSP.2004:377-380) thought of Tian is applied to three pixels and four vectors that pixel is formed.Such benefit one is to strengthen extendible difference number, the 2nd, reduced the shared space of Location Map.

In a word, in the existing method, the capacity of embedding is less, and when embedding the high capacity watermark, can't guarantee the quality of image, therefore, the present invention utilizes the complexity difference of image different piece, zones of different is carried out self-adaptation embed, to reach bigger embedding capacity and better image quality.

Summary of the invention

The technical problem to be solved in the present invention is: overcome the deficiencies in the prior art, a kind of reversible image watermark based on the expansion of self-adaptation difference is provided, this method can provide bigger embedding capacity and better pictures quality under the more situation of embed watermark data.

The technical solution adopted for the present invention to solve the technical problems: a kind of reversible image watermark based on the expansion of self-adaptation difference comprises watermark embed process, watermark extraction process and image recovery process; Before watermark embeds, need carry out pre-service to original image earlier, comprise alternately division, difference calculating, the computed image complexity also is provided with parameter; In telescopiny, use the self-adaptation expansion to carry out corresponding watermark and embed; Then will embed additional information, watermark data meeting and additional information are mixed embedding under specific circumstances; In leaching process, at first to carry out additional information and extract, difference is calculated; Then carry out the inverse operation of self-adaptation difference expansion, carry out the watermark data extraction and the original difference of reducing; Carry out the reduction of image by the difference after the reduction at last;

Preprocessing process is:

(1) alternately divides.Original image is replaced division, obtain two disjoint set of pixels S ₁And S ₂, and be that each pixel in the image makes up context, so that the realization that calculating of image complexity afterwards and pixel are estimated.

(2) difference is calculated.Use the context that constructs in the preprocessing process step (1), the estimated value of each pixel in the computed image, then by with original pixels relatively ask difference, this step can be divided into two parts successively: first at first, utilize S ₁In pixel estimate S ₂In pixel, this moment S ₁Be original pixel value; Second portion utilizes S then ₂In pixel estimate S ₁In pixel, this moment S ₂Be the pixel value behind the embed watermark.

(3) parameter setting.This step mainly comprises the parameter setting of two aspects, at first at original image, utilizes the statistical property of generalized Gaussian distribution, calculates the feature description α of image complexity; Then,, determine two parameter c and T that the self-adaptation difference is expanded according to the embed watermark data what, last, when user's demand can not be satisfied, can also judge whether that report embeds failure.

In watermark embed process, it is the process that by the self-adaptation expansion user data is embedded carrier image after parameter is determined that watermark data embeds.Under specific situation, watermark data can and additional information mixing and be embedded into, at this moment just need to guarantee watermark data and additional information can be distinguished can be synchronous.Additional information embeds.After user data (perhaps partial information) is embedded into, some additional informations are generally all arranged, comprise parameter c here, the location tables of T and recording pixel flooding information also needs to adopt certain strategy that they are embedded in the carrier image thus.It is necessary that these additional informations start watermark extracting often, thereby need guarantee that it can be calculated at first when watermark extracting.A simple strategy as shown in Figure 2, it is embedded into additional information in the image edge pixels in the mode that LSB (least significant bit (LSB)) replaces.

Extraction and image restoring process based on the reversible image watermark of self-adaptation difference expansion are:

1) additional information is extracted.This requirements of process at first obtains starting the necessary additional information of watermark extracting, two parameter c and the T that comprise the expansion of self-adaptation difference, the image complexity parameter alpha, in addition, if watermark extracting starts, it also will obtain carrying out the additional information that next step watermark extracting instructs is location tables information.For example, need know whether next difference was expanded, need perhaps to know which kind of situation is identifying the end of all leaching process.

2) difference is calculated.Difference in this process and the watermark embed process is calculated identical, and the difference will accomplish and embed the time is calculated and accomplished to mate fully.Here, mainly be that the division of pixel is consistent, it should be noted that watermark is first S when embedding ₂Back S ₁, and be first S when extracting ₁Back S ₂

3) watermark data extracts.This step mainly is to carry out the inverse operation of self-adaptation difference expansion to extract the data and the original difference of reducing.When extracting operation, need the information in the reference position table, because some pixel is not because overflow problem carries out the watermark embedding.

4) image restoring.This step need revert to original state with current pixel by the difference after the reduction with reference to environment.

The advantage that the present invention is compared with prior art had is:

(1) the present invention obtains the different complexity of image each several part by generalized Gaussian distribution, the watermark of carrying out adaptive difference expansion embeds and extracts, promptly carry out a spot of embedding in the zone of image complexity, carry out the embedding of mass data in the zone that image is mild, compare classic method, obviously increased the embedding capacity of watermark and improved visual quality for images.

(2) the present invention since complete structure context, therefore when identical PSNR, improved capacity distortion ratio than conventional method.

(3) the present invention when the embed watermark data are more, can obtain better effect by embedding more watermark in the image plateau region.

Description of drawings

Fig. 1 is the reversible image watermark method synoptic diagram of the expansion of the self-adaptation difference among the present invention;

Fig. 2 embeds synoptic diagram for additional information among the present invention;

Fig. 3 is watermark context model synoptic diagram among the present invention;

Fig. 4 alternately divides synoptic diagram for watermarking images among the present invention;

Fig. 5 is level and vertical direction context synoptic diagram among the present invention.

Embodiment

The present invention is at first based on the Gaussian statistics characteristic, obtain image complexity, proposing a kind of new pixel then divides promptly alternately to divide and obtains the context estimation model, and then obtain the estimation difference, utilize existing image complexity and estimation difference to carry out the expansion of self-adaptation difference at last, realize the embedding and the extraction of watermark.

As shown in Figure 1, overall flow of the present invention comprises watermark embedded part and watermark extracting part two large divisions.The watermark embedded part comprises preprocessing process and watermark embed process; Watermark extracting partly comprises extracts preprocessing process and watermark extraction process.Before watermark embeds, need carry out pre-service to original image earlier, preprocessing process comprises alternately division, difference calculating, the computed image complexity also is provided with parameter, shown in the upper left side frame of broken lines among Fig. 1; In watermark embed process, use the self-adaptation expansion to carry out corresponding watermark and embed, then will embed additional information, shown in the side frame of broken lines in lower-left among Fig. 1.In extracting preprocessing process, at first to carry out additional information and extract, difference is calculated, and then carries out the inverse operation of self-adaptation difference expansion, carries out the watermark data extraction and the original difference of reducing, shown in the upper right side frame of broken lines among Fig. 1; In watermark extraction process, carry out the extraction of watermark and the reduction of image at last, shown in the right side frame of broken lines among Fig. 1 by the difference after the reduction.Introduce above-mentioned four processes below respectively in detail, promptly preprocessing process and watermark embed process extract preprocessing process and watermark extraction process.

1. as shown in Figure 1, preprocessing process specific implementation step of the present invention is as follows:

Step 1: alternately divide.At adaptive difference expansion, need to use current pixel all pixels on every side to make up context model.Consider in eight pixels around the current pixel, with current pixel immediate be laterally with four pixels longitudinally, it is hereinafter textural therefore to use these four pixels, as shown in Figure 3.Make original image be

L={x (i, j) | 1≤i≤H, 1≤j≤W}, i, j are pixel coordinate position (1)

Wherein H and W distinguish the height and width of presentation video.In order to give each pixel hereinafter textural, at first all pixels in the original image are divided into two disjoint set of pixels S ₁And S ₂, promptly alternately divide, as shown in Figure 4, set of pixels by

$\left\{\begin{array}{c}{S}_1=\left\{x(i,j)\right|\left(i\mathrm{mod}2\right)\⊕\left(j\mathrm{mod}2\right)=0\}\\ S_2=\left\{x(i,j)\right|\left(i\mathrm{mod}2\right)\⊕\left(j\mathrm{mod}2\right)=1\}\end{array}\right.---\left(2\right)$

Expression respectively, 1≤i≤H1≤j≤W wherein, mod be for getting remainder, Be XOR, because the pixel of image border (showing with grey among Fig. 4) does not have complete context, so the present invention does not carry out the difference expansion to it.

Step 2: difference is calculated.At the context that has built, calculate the estimated value of each pixel in the image, then by with original pixels relatively obtain its difference.This step can be divided into two parts successively, and at first first utilizes S ₁In pixel estimate S ₂In pixel, this moment S ₁Be original pixel value; Second portion utilizes S then ₂In pixel estimate S ₁In pixel, this moment S ₂Be the pixel value behind the embed watermark.

Specific algorithm is divided into the level and the vertical direction of quadrature with four neighbor pixels, and as shown in Figure 5, neighbor pixel is respectively x _u, x _d, x _l, x _r, calculate the mean value of two pixels of each direction respectively, and give the different weights of these two mean values and estimate with pixel x to the center.The mean value of both direction by formula (3) calculates.

$\left\{\begin{array}{c}{x}_v=\frac{(x_u+x_d)}{2}\\ x_h=\frac{(x_l+x_r)}{2}\end{array}\right.---\left(3\right)$

Make vertical direction mean value x _vWith horizontal direction mean value x _hWeights be w _v, w _h, then

Account form be

$\left\{\begin{array}{c}\hat{x}=w_v\·x_v+w_h\·x_h\\ w_v+w_h=1\end{array}\right.---\left(4\right)$

Make σ (h) and σ (v) be respectively the mean square deviation of level and vertical direction, by formula

$\left\{\begin{array}{c}\mathrm{\σ}\left(v\right)=\frac{1}{3}\underset{k=1}{\overset{3}{\mathrm{\Σ}}}{(S_v\left(k\right)-x_{\mathrm{avg}})}^2\\ \mathrm{\σ}\left(h\right)=\frac{1}{3}\underset{k=1}{\overset{3}{\mathrm{\Σ}}}{(S_h\left(k\right)-x_{\mathrm{avg}})}^2\end{array}\right.---\left(5\right)$

Calculate, wherein x _AvgMean value for adjacent pixel

$x_{\mathrm{avg}}=\frac{x_u+x_d+x_l+x_r}{4}---\left(6\right)$

And order S _vAnd S _hBe collection of pixels:

$\left\{\begin{array}{c}{S}_v=\{x_u,x_v,x_d\}\\ S_h=\{x_l,x_h,x_r\}\end{array}\right.---\left(7\right)$

Weight w _vAnd w _hComputing method be

$w_v=\frac{\mathrm{\σ}\left(h\right)}{\mathrm{\σ}\left(h\right)+\mathrm{\σ}\left(v\right)},w_h=1-w_v---\left(8\right)$

When the estimated value that obtains pixel

After, can basis

$e=x-\hat{x}---\left(9\right)$

Calculate the evaluated error of x.S ₁And S ₂The set E that forms of evaluated error ₁And E ₂For

$\left\{\begin{array}{c}{E}_1=\left\{e(i,j)\right|x(i,j)\∈S_1\}\\ E_2=\left\{e(i,j)\right|x(i,j)\∈S_2\}\end{array}\right.---\left(10\right)$

Wherein (i j) is pixel x (i, evaluated error j) that calculates by formula (9) to e.These two evaluated error set will be used to the embed watermark data.

Step 3: parameter setting.This step mainly comprises the parameter setting of two aspects, at first calculates image complexity; Then,, determine two parameter c and T that the self-adaptation difference is expanded according to the embed watermark data what, last, when user's demand can not be satisfied, can also judge whether that report embeds failure.

The estimation of image complexity: the form parameter of density function of utilizing the generalized Gaussian distribution (GGD) in the wavelet field is as the measurement parameter of image complexity, the estimation of image complexity is converted to the estimation of the form parameter of pair-density function thus, estimate by curve-fitting method, and order finally to obtain estimated value be image complexity α.

For generalized Gaussian distribution, generally consider the situation of Gaussian distribution average u=0.If x=is (x ₁, x ₂..., x _n) be a sample from the overall X of GGD of average u=0, because GGD is symmetrically distributed, its first moment about the origin is zero, so can adopt absolute moment to calculate.

When u=0, the single order absolute moment is

$m_1=E\left\{\right|x\left|\right\}$

$={\∫}_{-\∞}^{+\∞}\left|x\right|\frac{\mathrm{\α}}{2\mathrm{\β\Γ}(1/\mathrm{\α})}{e}^{-{\left|\frac{x}{\mathrm{\β}}\right|}^{\mathrm{\α}}}\mathrm{dx}---\left(11\right)$

$=\frac{\mathrm{\α}}{\mathrm{\β\Γ}(1/\mathrm{\α})}{\∫}_0^{+\∞}\left|x\right|e^{-{\left|\frac{x}{\mathrm{\β}}\right|}^{\mathrm{\α}}}\mathrm{dx}$

Order Get x=β y ^{1/ α}, The above-mentioned formula of substitution gets

$\frac{\mathrm{\α}}{\mathrm{\β\Γ}(1/\mathrm{\α})}{\∫}_0^{+\∞}{y}^{\frac{2}{a}-1}{e}^{-y}\mathrm{dy}=\mathrm{\β}\frac{\mathrm{\Γ}(2/\mathrm{\α})}{\mathrm{\Γ}(1/\mathrm{\α})}---\left(12\right)$

Again will

Substitution gets

$m_1=E\left\{\right|x\left|\right\}=\mathrm{\σ}\frac{\mathrm{\Γ}(2/\mathrm{\α})}{\sqrt{\mathrm{\Γ}(1/\mathrm{\α})\mathrm{\Γ}(3/\mathrm{\α})}}---\left(13\right)$

In like manner can get second moment is

m ₂＝E{|x ²|}＝σ ² (14)

And

Note

$R\left(\mathrm{\α}\right)=\frac{E^2\left\{\right|X\left|\right\}}{E\left\{X^2\right\}}=\frac{{\mathrm{\Γ}}^2(2/\mathrm{\α})}{\sqrt{\mathrm{\Γ}(1/\mathrm{\α})\mathrm{\Γ}(3/\mathrm{\α})}}=\frac{m_1^2}{m_2}---\left(15\right)$

R (α) is called the Generalized Gaussian parameter compares function; And m ₁, m ₂Estimation

Can obtain by following formula

${\hat{m}}_1=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\left|x_i\right|,$ ${\hat{m}}_2=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{x}_i^2---\left(16\right)$

Thereby form parameter α is estimated as

$\widehat{\mathrm{\α}}=R^{-1}\left(\frac{{\hat{m}}_1^2}{{\hat{m}}_2}\right)---\left(17\right)$

Wherein

$R\left(x\right)=\frac{{\mathrm{\Γ}}^2(2/x)}{\sqrt{\mathrm{\Γ}(1/x)\mathrm{\Γ}(3/x)}}---\left(18\right)$

The inverse function R of R (x) ^-1(x) analytic expression is difficult to try to achieve, so adopt the method for numerical fitting.

Hyperbolic function match original function R (x) then sets up model of fit y=a+b/x, adopts least square fitting, gets antiderivative approximating function and is

$y=0.77127-\frac{0.26961}{x}$

Thereby the original function of approximating function is

$R^{-1}\left(x\right)=\frac{-0.26961}{x-0.77127}---\left(19\right)$

So model of fit is

Utilize least square method to try to achieve contrafunctional fitting function to be

$R^{-1}\left(x\right)=\frac{0.2718}{0.7697-x}-0.1247---\left(20\right)$

In sum, obtain the estimation of parameter alpha by formula (17) and (20), and then obtained image complexity.

Determine the two parameter constant c and the capacity of the embedding T of the expansion of self-adaptation difference, as shown in Figure 5, the steady degree in zone is passed through variances sigma ²Obtain,

$\left\{\begin{array}{c}{\mathrm{\σ}}^2=\frac{1}{4}\underset{k=1}{\overset{4}{\mathrm{\Σ}}}{(x_i\left(k\right)-x_{\mathrm{avg}})}^2\\ x=\{x_l,x_r,x_u,x_d\}\end{array}\right.---\left(21\right)$

Wherein

$x_{\mathrm{avg}}=\frac{x_u+x_d+x_l+x_r}{4}---\left(22\right)$

Thus, the radix of difference expansion can pass through

Wherein, c is a constant, and α is the complexity of carrier image, and T is a threshold value, and c and T are used for the embedding capacity of watermark of control chart picture.In order to prevent that pixel from overflowing seriousization, need to guarantee 2≤T≤10, for constant c, in general, its value is big more, and the watermark of embedding is many more but distortion image is serious more, and vice versa.

2. watermark embed process

In watermark embed process, by image complexity α, self-adaptation spreading parameter c and T determine self-adaptation expansion radix b _IjSuppose w represent length be l treat the water mark inlaying data, in order to express easily, make w=w ₁, w ₂..., w _n, wherein n=l/8 and w ₁, i=1,2 ... n comprises 8 bit binary data.Next provide its detailed performing step:

(2.1) determine self-adaptation expansion radix b _Ij, by image complexity α, and self-adaptation spreading parameter c and T, calculate by formula

Determine self-adaptation expansion radix b _Ij, σ wherein ²Be variance, be used to weigh the steady degree in zone;

(2.2) suppose that w represents that length is the scale-of-two watermark data for the treatment of embedding of l, the piece with w is divided into n 8 makes w=w ₁, w ₂..., w _n, wherein n=l/8 and order w _t, t=1,2 ... n comprises 8 bit binary data;

(2.3) read watermark data w _t, and be converted into decimal system w _Dt, in order to judge w _DtWhether embedding finishes, and provides variable u and identifies, and initialization u is 1;

(2.4) scanning carrier image is at concrete pixel x _Ij, known estimated value x _Ij', self-adaptation expansion radix b _Ij, estimate difference e _Ij, pass through r _{I, j}=w _{D, r}Mod b _IjObtain final embedding content r herein _{I, j}, utilize formula then

$e^{\′}=\left\{\begin{array}{cc}e\×b+r,& b\≠1\\ e,& b=1\end{array}\right.---\left(24\right)$

Carry out self-adaptation and embed, thereby obtain embedding back pixel value x _Ij";

In the self-adaptation telescopiny in (2.4), "＞255 or x "＜0 if x, the watermark of then skipping this pixel embeds, and utilizes location tables to write down this locations of pixels and pixel flooding information;

(2.5) after step (2.4) embeds and finishes, upgrade

U=u * b _Ij, if next watermark data w is read in u＞255 _T+1And put u=1, change step (2.3) over to and embed the next bit watermark; Otherwise change step (2.4) over to and continue w _{D, t}Embedding;

(2.6) carry out additional information and embed, described additional information comprises parameter c, T, and parameter alpha is the location tables of recording pixel flooding information in image complexity and the telescopiny (2.5); So far watermark embed process finishes, and obtains embedding the back image;

Watermark is carried out the additional information telescopiny after embedding and finishing.Additional information comprises parameter c, the location tables of T and recording pixel flooding information.These additional informations are that the startup watermark extracting is necessary, thereby need guarantee that it can be calculated at first when watermark extracting.A simple strategy is embedded into additional information in the image edge pixels in the mode that LSB replaces as shown in Figure 2.So far watermark embeds and finishes, and obtains embedding the back image.

3. watermark extracting partly comprises the extraction preprocessing process.

Extract preprocessing process, at first extract additional information, this flow process is the inverse process that additional information embeds.

(3.1) at first carry out the additional information leaching process, this process is the inverse process that additional information embeds, obtain two parameter c and the T of the expansion of self-adaptation difference, image complexity parameter alpha and location tables, if for example use LSB (least significant bit (LSB)) mode with reversible being embedded in the image edge pixels of additional information, then can use the watermark extracting algorithm of LSB, additional information is extracted from image edge pixels;

(3.2) then by with preprocessing process that watermark embeds in identical division carry out difference and calculate, this process is calculated identical with the difference in the preprocessing process (2.1), and the difference when accomplishing and embedding is calculated and is accomplished to mate fully, the division that is pixel is consistent, and calculating is first set of computations S because watermark embeds time difference value ₂Difference, use S then ₂The result calculate S ₁Difference, so be to calculate S earlier when extracting ₁Difference, calculate S then ₂Difference;

4. watermark extraction process

Watermark extraction process is watermark extracting and image restoring process: the extraction by additional information has obtained image complexity α, self-adaptation spreading parameter c, T and location tables, and after utilizing contextual structure to obtain the estimation difference of embedded images, next provide its detailed performing step:

Described watermark extraction process is the inverse process of telescopiny, and concrete steps are as follows:

(4.1) initialization marking variable u is 1, because the embedding of watermark is not carried out in the zone, image border, so to the zone of i=1 or j=1, image pixel remains unchanged, and directly reduction;

(4.2) scanning embedded images is at concrete pixel x ", known estimated value x ', self-adaptation expansion radix b _Ij, the estimation difference e ' _Ij, pass through formula

r _ij＝e _ij′％b _ij，b _ij≠1

Obtain embed watermark r herein _{I, j}, utilize formula then

Recover original difference e _Ij, and then obtain the image original pixels;

In the leaching process in (4.2),, then skip the watermark extracting of this position, keep pixel constant simultaneously if write down this position in the location tables.

(4.3) after step (4.2) is extracted and is finished, with r _{I, j}, b _IjPut into set R respectively _t, B _tIn, r _{I, j}, b _IjIn i, j is a coordinate, it is at R _t, B _tIn the position be the order of putting into, and upgrade u=u * b _Ij

If u＞255, the size that note is gathered R this moment is m, passes through formula

$w_t\left(i\right)=\left\{\begin{array}{cc}{w}_t(i+1)\&times;B\left(i\right)+R\left(i\right),& 2\&le;i<m\\ 1,& i=m\end{array}\right.$

Then complete watermark w _t=w _t(1), puts u=1 again, carry out step (4.2) and extract next bit watermark w _T+1, if step (4.2) item is proceeded in u≤255.

The content that is not described in detail in the instructions of the present invention belongs to this area professional and technical personnel's known prior art.

Claims (3)

1. reversible image watermark method based on self-adaptation difference expansion, it is characterized in that: comprise watermark embedded part and watermark extracting part two large divisions, the watermark embedded part comprises preprocessing process and watermark embed process; Watermark extracting partly comprises extracts preprocessing process and watermark extraction process;

Described preprocessing process is:

(1.1) alternately divide, construct the context relation of pixel, original image is divided into disjoint set of pixels S ₁And S ₂, be used for the calculating of image complexity and the realization that pixel is estimated;

(1.2) difference is calculated, and uses the context that constructs in the preprocessing process step (1), the estimated value of each pixel in the computed image, then by with original pixels relatively ask difference, this step can be divided into two parts successively: first at first, utilize S ₁In pixel estimate S ₂In pixel, this moment S ₁Be original pixel value; Second portion utilizes S then ₂In pixel estimate S ₁In pixel, this moment S ₂Be the pixel value behind the embed watermark;

(1.3) parameter setting, comprise the parameter setting of two aspects, at first at original image, the form parameter α of density function that utilizes the generalized Gaussian distribution (GGD) in the wavelet field is as the measurement parameter of image complexity, and by curve-fitting method parameter alpha is estimated, parameter alpha is defined as image complexity; Then, according to what of embed watermark data, determine two parameter c and the T of self-adaptation difference expansion, c and T are similar to the parameter used in the generality expansion, are used for the embedding capacity of watermark of control chart picture;

Described watermark embed process:

(2.1) determine self-adaptation expansion radix b _Ij, by the parameter alpha that preprocessing process step (1.3) obtains, promptly image complexity reaches self-adaptation spreading parameter c and T, calculates by formula

Determine self-adaptation expansion radix b _Ij, i, j are coordinate, σ ²Be variance, be used to weigh the steady degree in zone;

(2.2) suppose that w represents that length is the scale-of-two watermark data for the treatment of embedding of l, the piece with w is divided into n 8 makes w=w ₁, w ₂..., w _n, wherein n=l/8 and w _t, t=1,2 ... n comprises 8 bit binary data;

(2.3) read watermark data w _t, and be converted into decimal system w _{D, t}, in order to judge w _{D, t}Whether embedding finishes, and provides variable u and identifies, and initialization u is 1;

(2.4) scanning carrier image is at concrete pixel x _Ij, known estimated value x _Ij', self-adaptation expansion radix b _Ij, estimate difference e _Ij, pass through r _{I, j}=w _{D, t}Mod b _IjObtain final embedding content r herein _{I, j}Utilize formula then

$e^{\&prime;}=\left\{\begin{array}{cc}e\&times;b+r,& b=1\\ e,& b=1\end{array}\right.$

Carry out self-adaptation and embed, thereby obtain embedding back pixel value x _Ij";

In the self-adaptation telescopiny in (2.4), "＞255 or x "＜0 if x, the watermark of then skipping this pixel embeds, and utilizes location tables to write down this locations of pixels and pixel flooding information;

(2.5) after step (2.4) embeds and finishes, upgrade

U=u * b _Ij, if next watermark data w is read in u＞255 _T+1And put u=1, change step (2.3) over to and embed the next bit watermark; Otherwise change step (2.4) over to and continue w _{D, t}Embedding;

(2.6) carry out additional information and embed, described additional information comprises parameter c, T, and parameter alpha is the location tables of recording pixel flooding information in image complexity and the telescopiny (2.5); So far watermark embed process finishes, and obtains embedding the back image;

Described extraction preprocessing process:

(3.1) at first carry out the additional information leaching process, this process is the inverse process that additional information embeds, and obtains two parameter c and the T of self-adaptation difference expansion, image complexity parameter alpha and location tables;

(3.2) carrying out difference by division identical in the preprocessing process (1.1) then calculates, this process is calculated identical with the difference in the preprocessing process (2.1), and the difference when accomplishing and embedding is calculated and is accomplished to mate fully, the division that is pixel is consistent, and calculating is first S because watermark embeds time difference value ₂Back S ₁So, be first S when extracting ₁Back S ₂

Described watermark extraction process is the inverse process of telescopiny, and concrete steps are as follows:

(4.1) initialization marking variable u is 1, because the embedding of watermark is not carried out in the zone, image border, so to the zone of i=1 or j=1, image pixel remains unchanged, and directly reduction;

(4.2) scanning embedded images is at concrete pixel x ", known estimated value x ', self-adaptation expansion radix b _Ij, the estimation difference e ' _Ij, pass through formula

r _ij＝e _ij′％b _ij，b _ij≠1

Obtain embed watermark r herein _Ij, utilize formula then

Recover original difference e _Ij, and then obtain the image original pixels;

In the leaching process in (4.2),, then skip the watermark extracting of this position, keep pixel constant simultaneously if write down this position in the location tables.

(4.3) after step (4.2) is extracted and is finished, with r _{I, j}, b _IjPut into set R respectively _t, B _tIn, r _{I, j}b _IjIn i, j is a coordinate, it is at R _t, B _tIn the position be the order of putting into, and upgrade u=u * b _Ij

If u＞255, the size that note is gathered R this moment is m, passes through formula

$w_t\left(i\right)=\left\{\begin{array}{cc}{w}_t(i+1)\&times;B\left(i\right)+R\left(i\right),& 2\&le;i<m\\ 1,& i=m\end{array}\right.$

Then complete watermark w _t=w _t(1), puts u=1 again, carry out step (4.2) and extract next bit watermark w _T+1, if step (4.2) item is proceeded in u≤255.

2. the reversible image watermark method based on the expansion of self-adaptation difference according to claim 1, it is characterized in that: the value of described T is 2≤T≤10.

3. the reversible image watermark method based on the expansion of self-adaptation difference according to claim 1 is characterized in that: described additional information is to be embedded in the image edge pixels in the mode that LSB (Least Significant Bit least significant bit (LSB)) replaces.

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