CN102147912B - 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 self-adaptation difference expansion

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 self-adaptation difference expansion.

Background technology

The numerical information revolution has brought deep variation to the mankind's society and life, and various Digital Medias works have brought new challenge when enriching people's lives.The digital multimedia works copy and distribute the cheap of cost, make digital piracy very general, so the safety problems such as digital copyright protecting and content integrity checking become problem in the urgent need to address.Digital watermarking in digital picture, text, video or sound signal, has become a kind of effective means of protecting digital media content safety by some Information hiding.In most of existing digital watermark methods, host's media are because the embedding of watermark signal can and can't revert to embed watermark state before by permanent change.Although the distortion that watermark is introduced is not easy to be discovered by people's sensory perceptual system usually, but in some require high special dimension to the data fidelity, 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 is limited by very large.In order to address this problem, people have proposed the concept of reversible digital watermarking, and this watermarking project, in embed watermark information protection digital publishing rights, can guarantee the virgin state when the extraction end is not embedded into watermark by host's media precise restoration to it.Since Barton in 1997 proposes the concept of reversible water mark for the first time, existing scholar proposes some reversible water mark algorithms in recent years.Existing reversible water mark algorithm consists essentially of two kinds of modes, in spatial domain, embeds and embeds in frequency domain.

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

Algorithm based on compression of images generally adopts lossless compression algorithm that image is compressed with vacating space and carrys out embed watermark in the insensitive part of human eye, embedding capacity depends on compressibility, embedding capacity is generally little, and efficient compression of images computing complexity, so this class watermark computation complexity is higher; Reversible image algorithm based on histogram modification is the statistical property of utilizing image pixel, carries out histogram and moves and obtain embedded space; Algorithm based on 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, the data of two can be embedded and wherein and not causes obvious distortion.

Based on difference expansion (Difference Expansion, DE) reversible image watermark, the earliest by Tian 2003, propose for the first time (referring to J.Tian.Reversible data embedding using a difference expansion[J] .IEEE Trans.Circuits Systems and Video Technology.2003,13 (8): 890-896).Difference expansion, also can be regarded as a kind of integer wavelet transformation, the HFS of its expansion wavelet transformation, and watermark information is embedded 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, the binary form of the difference d ' after embed watermark is shown (d _N-1d _N-2... d ₁d ₀B) ₂.This is equivalent to d, to one of left dislocation, then watermark b to be embedded in the lowest order of vacating.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 forms.Such benefit one is to strengthen extendible difference number, the 2nd, reduced the shared space of Location Map.

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

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 self-adaptation difference expansion is provided, the method, in the situation that the embed watermark data are more, can provide larger embedding capacity and better picture quality.

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

Preprocessing process is:

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

(2) difference is calculated.Use the context constructed in preprocessing process step (1), the estimated value of each pixel in computed image, then by with original pixels relatively ask difference, this step can be divided into successively two parts: first at first, utilize S ₁In pixel estimate S ₂In pixel, S now ₁For original pixel value; Then second portion, utilize S ₂In pixel estimate S ₁In pixel, S now ₂For the pixel value after embed watermark.

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

In watermark embed process, it is to expand by self-adaptation the process that user data is embedded to carrier image after parameter is determined that watermark data embeds.In specific situation, watermark data can and additional information mixing and be embedded into, at this moment just needing assurance watermark data and additional information to distinguish can be synchronous.Additional information embeds.After user data (or partial information) is embedded into, some additional informations are generally arranged, comprise parameter c here, the position table of T and recording pixel flooding information, also need to adopt certain strategy that they are embedded in carrier image thus.It is necessary that these additional informations start watermark extracting often, thereby need to guarantee that it can be calculated at first when watermark extracting.As shown in Figure 2, it is replaced additional information mode with LSB (least significant bit (LSB)) is embedded in image edge pixels a simple strategy.

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

1) additional information is extracted.At first this requirements of process obtains starting the necessary additional information of watermark extracting, two parameter c and the T that comprise the self-adaptation difference expansion, 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 instructed is position table information.For example, need to know whether next difference was expanded, or need to know which kind of situation is identifying the end of all leaching process.

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

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

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

The advantage that the present invention 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, carry out a small amount of embedding in the zone of image complexity, the embedding of mass data is carried out in the zone mild at image, compare classic method, obviously increased the embedding capacity of watermark and improved the visual quality of image.

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

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

The accompanying drawing explanation

The reversible image watermark method schematic diagram that Fig. 1 is the self-adaptation difference expansion in the present invention;

Fig. 2 is that in the present invention, additional information embeds schematic diagram;

Fig. 3 is watermark context model schematic diagram in the present invention;

Fig. 4 be in the present invention watermarking images alternately divide schematic diagram;

Fig. 5 is horizontal and vertical direction context schematic diagram in the present invention.

Embodiment

The present invention is at first based on the Gaussian statistics characteristic, obtain image complexity, then proposing a kind of new pixel division alternately divides and obtains the context estimation model, and then obtain the estimation difference, finally utilize existing image complexity and estimation difference to carry out the self-adaptation difference expansion, realize 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, first need original image is carried out to pre-service, preprocessing process comprises alternately division, difference calculating, computed image complexity parameters, as shown in dotted line frame in upper left side in Fig. 1; In watermark embed process, use the self-adaptation expansion to carry out corresponding watermark embedding, then will embed additional information, in lower dashed line frame as left as Fig. 1 as shown in.In extracting preprocessing process, at first carry out the additional information extraction, difference is calculated, and then carries out the inverse operation of self-adaptation difference expansion, carries out the watermark data extraction and reduces original difference, as shown in dotted line frame in upper right side in Fig. 1; Finally in watermark extraction process, by the difference after reduction, carry out the extraction of watermark and the reduction of image, as shown in dotted line frame in right side in Fig. 1.Below introduce in detail respectively above-mentioned Four processes, 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.For adaptive difference expansion, need build context model by all pixels around current pixel.Consider in eight pixels around current pixel, with current pixel immediate be laterally with four pixels longitudinally, therefore next hereinafter textural by these four pixels, as shown in Figure 3.Make original image be

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

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

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

Mean respectively, 1≤i≤H1≤j≤W wherein, mod is the remainder number,

For XOR, because the pixel of image border (showing with grey in Fig. 4) does not have complete context, so the present invention does not carry out difference expansion to it.

Step 2: difference is calculated.For the context built, calculate the estimated value of each pixel in image, then by with original pixels relatively obtain its difference.This step can be divided into successively two parts, and at first first, utilize S ₁In pixel estimate S ₂In pixel, S now ₁For original pixel value; Then second portion, utilize S ₂In pixel estimate S ₁In pixel, S now ₂For the pixel value after embed watermark.

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

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

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

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, press formula

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

Calculated, 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 _hFor the pixel set:

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

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

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

The evaluated error that wherein e (i, j) is the pixel x (i, j) that calculates by formula (9).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, according to the embed watermark data the number, determine two parameter c and the T of self-adaptation difference expansion, last, when user's demand can not be satisfied, can also judge whether that report embeds unsuccessfully.

The estimation of image complexity: utilize the form parameter of density function of the generalized Gaussian distribution (GGD) in wavelet field as the parameter of measurement of image complexity, the estimation of image complexity is converted to the estimation of the form parameter of pair-density function thus, by curve-fitting method, estimated, 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 the sample from the overall X of GGD of average u=0, because GGD is symmetrical, its first moment about the origin is zero, therefore can adopt absolute moment to be calculated.

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

Obtain x=β y ^{1/ α},

The above-mentioned formula of substitution obtains

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

Again will Substitution obtains

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

In like manner can obtain 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 to the Generalized Gaussian parameter ratio function; And m ₁, m ₂Estimation

Can be obtained 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, therefore adopt the method for numerical fitting.

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

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

Thereby the original function of approximating function is

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

Therefore 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 two parameter constant c and the embedding capacity T of self-adaptation difference expansion, 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 constant, the complexity that α is carrier image, and T is threshold value, 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 larger, and the watermark of embedding is more but distortion image is more serious, 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 _Ij.Suppose w mean 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, by formula, calculate

Determine self-adaptation expansion radix b _Ij, σ wherein ²For variance, for weighing the steady degree in zone;

(2.2) suppose that w means the scale-of-two watermark data for the treatment of embedding that length is l, the piece that w is divided into to n 8, make w=w ₁, w ₂..., w _n, wherein n=l/8 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 embed completely, provide variable u and identified, initialization u is 1;

(2.4) scanning carrier image, for 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}, then utilize formula

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

Carry out the self-adaptation embedding, thereby obtain embedding rear pixel value x _Ij";

In self-adaptation telescopiny in (2.4), if x ">255 or x "<0, the watermark of skipping this pixel embeds, and utilizes position and the pixel flooding information of position this pixel of table record;

(2.5), after step (2.4) has embedded, upgrade

U=u * b _IjIf u>255, read in next watermark data w _T+1Juxtaposition u=1, proceed to step (2.3) and embed the next bit watermark; Otherwise proceed to step (2.4) and continue w _{D, t}Embedding;

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

After watermark has embedded, carry out the additional information telescopiny.Additional information comprises parameter c, the position table of T and recording pixel flooding information.These additional informations are that the startup watermark extracting is necessary, thereby need to guarantee that it can be calculated at first when watermark extracting.As shown in Figure 2, the mode that additional information is replaced with LSB is embedded in image edge pixels a simple strategy.So far watermark embeds and finishes, and obtains embedding rear 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 self-adaptation difference expansion, image complexity parameter alpha and position table, if for example use being embedded in image edge pixels that additional information is reversible of LSB (least significant bit (LSB)) mode, can use the watermark extracting algorithm of LSB, additional information is extracted from image edge pixels;

(3.2) then by division identical in the preprocessing process embedded with watermark, carry out difference calculating, this process is calculated identical with the difference in preprocessing process (2.1), and accomplish that difference when embedding is calculated and accomplish to mate fully, the division that is pixel is consistent, because watermark embeds time difference value, calculating is first set of computations S ₂Difference, then use S ₂Result calculate S ₁Difference, so be first to calculate S while extracting ₁Difference, then calculate S 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 position table, and, after utilizing contextual structure to obtain the estimation difference of embedded images, next provide its detailed performing step:

Described watermark extraction process, be 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, direct-reduction;

(4.2) scanning embedded images, for 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}, then utilize formula

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

In leaching process in (4.2), if recorded this position in the table of position, skip the watermark extracting of this position, keep pixel constant simultaneously.

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

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

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

Complete watermark w _t=w _t(1), again put u=1, carry out step (4.2) and extract next bit watermark w _T+1If step (4.2) is proceeded in u≤255 item.

The content be not described in detail in instructions of the present invention belongs to the known prior art of professional and technical personnel in the field.

Claims (3)

1. the reversible image watermark method based on the 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 to disjoint set of pixels S ₁And S ₂, the realization of estimating for calculating and the pixel of image complexity;

(1.2) difference is calculated, and uses the context constructed in preprocessing process step (1.1), the estimated value of each pixel in computed image, then by with original pixels relatively ask difference, this step can be divided into successively two parts: first at first, utilize S ₁In pixel estimate S ₂In pixel, S now ₁For original pixel value; Then second portion, utilize S ₂In pixel estimate S ₁In pixel, S now ₂For the pixel value after embed watermark;

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

Described watermark embed process:

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

Determine self-adaptation expansion radix b _Ij, i, j is coordinate, σ ²For variance, for weighing the steady degree in zone;

(2.2) suppose that w means the scale-of-two watermark data for the treatment of embedding that length is l, the piece that w is divided into to n 8, make 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 embed completely, provide variable u and identified, initialization u is 1;

(2.4) scanning carrier image, for 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}Modb _IjObtain final embedding content r herein _{I, j}, then utilize formula

Carry out the self-adaptation embedding, thereby obtain embedding rear pixel value x _Ij";

In self-adaptation telescopiny in (2.4), if x _Ij">255 or x _Ij"<0, the watermark of skipping this pixel embeds, and utilizes position and the pixel flooding information of position this pixel of table record;

(2.5), after step (2.4) has embedded, upgrade

U=u * b _IjIf u>255, read in next watermark data w _T+1, juxtaposition u=1, proceed to step (2.3) and embed the next bit watermark; If proceeding to step (2.4), u≤255 continue w _{D, t}Embedding;

(2.6) carry out the additional information embedding, described additional information comprises parameter c, T, and parameter alpha is the position table of recording pixel flooding information in image complexity and telescopiny (2.4); So far watermark embed process finishes, and obtains embedding rear 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 position table;

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

Described watermark extraction process, be 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, direct-reduction;

(4.2) scanning embedded images, for concrete pixel x ", oneself knows 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, then utilize formula

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

In leaching process in (4.2), if recorded this position in the table of position, skip the watermark extracting of this position, keep pixel constant simultaneously;

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

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

Complete watermark w _t=w _t(1), again put u=1, carry out step (42) and extract next bit watermark w _T+1If step (4.2) is proceeded in u≤255 item.

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

Images