quality assessment of image and video with adaptive

QUALITY ASSESSMENT OF IMAGEAND VIDEO WITH ADAPTIVE

WATERMARK METHOD

Mohanakrishnan.P1∗, Dr.K.Suthendran2

Renuka.H3 Suthendran K2

1Department of Computer Science,2Department of Information Technology

1,2Kalasalingam Academy of Researchand Education,Krishnankoil,Tamilnadu.

3Mets school of engineering, Mala,Calicut University. Kerala

June 29, 2018

Abstract

Digital watermarking based quality evaluation emergesas a potential Reduced- or No-Reference quality metric,which estimates signal quality by assessing the degrada-tion of the embedded watermark. A tree structure basedscheme is projected to assign adaptive watermark embed-ding strengths by pre-estimating the signal degradation char-acteristics, which greatly improves the computational eciency.The SPIHT tree structure and HVS masking are used toguide the watermark embedding, which greatly reduces thesignal quality loss caused by watermark embedding. Exper-imental results reveal that the tree structure based schemecan estimate image and video quality with some high accu-racy in terms of PSNR and true detection rate. In the caseof video, video file is changed into frames and the water-mark image is embedded into any one of the frames. Theembedding process is carried out using DWT, yet again the

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International Journal of Pure and Applied MathematicsVolume 120 No. 6 2018, 49-76ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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embedded frame and other remaining frames are changedinto video file and it is transmitted. Watermark image isextracted from the video in the receiver side. Finally, byusing metrics such as MSE, PSNR the quality of both videoand watermark image is estimated under different distor-tion.

1 INTRODUCTION

As presented in [5], the watermark degradation in image is evalu-ated by comparing the distorted watermark and the original water-mark cannot represent the quality of the distorted image by itself,It is always experimentally related to the existing Full-Referencequality metrics like PSNR, SSIM or MOS. Thus, the choosing ofthe watermark embedding strengths directly affects the accuracyof the quality evaluation. depending on the type of the water-mark, the proposed watermarking based image quality evaluationschemes can be categorized into two groups: schemes with image-feature-independent watermarks and schemes with image-feature-dependent watermarks. Paper [6], a randomly generated binarywatermark is embedded into the middle-frequency coefficients inthe DCT domain using a look-up table method. With this method,each of the possible integer DCT coefficients is randomly assignedone binary bit. For one selected DCT coefficient, if the watermarkbit is different from its associated binary bit, this coefficient will bemodified to the closest DCT coefficient associated with the binarybit equals to the watermark bit, the experimental results show theembedded watermark degrades monotonically with the increasingof the distortion strength, significant quality loss to the cover imagea scheme is proposed in [10] in the DWT domain using a quanti-zation based scheme. With the quantization based method, theodd DWT coefficients are assigned binary bit 0. In contrast, theeven DWT coefficients are assigned binary bit 1. Compared to thescheme in [6], the scheme in [10] & [14] reduced the quality losscaused by the watermark embedding process, which is over 40 dBin PSNR.The research about the scheme in [10] is extended in [8],[9]&[11], In[9],propose a Ideal Mapping Curve and is quantitatively expressed.. The degradation of the watermark is evaluated by computing the

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International Journal of Pure and Applied Mathematics Special Issue

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True Detection Rate (TDR) of the distorted watermark comparedto the original watermark. In [8]&[9], the adaptive watermark em-bedding strength is iteratively tested in the watermark embeddingprocess.

schemes with image-feature-dependent watermarks, In[17], the authors proposed to use the statistical features extractedfrom the three-scale four-orientation steerable pyramid decomposedsubbands of the cover image as the watermark in [18]. The schemeis implemented using a relatively complicated Discrete Wavelet de-composition of the cover image. The experimental results show thatthe scheme works more efficiently with the additive white Gaussiannoise than JPEG compression.

2 Watermarking Based Video Quality

Evaluation

To solve these problems resulted by the Full-Reference metrics andMOS, the watermarking based video quality evaluation method isproposed as the Reduced- or No-reference video quality metric inliterature [12]&[13].

2.1 Schemes with Video-Feature-Independent Wa-termarks

In [22], a semi-fragile watermarking scheme is proposed to test thequality of the low-bit rate QCIF video sequence. The quality of thedistorted video signal is evaluated by calculating the approximatedPSNR, a watermarking based video quality metric is implementedin DCT domain for the high bit-rate video sequences [23].

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2.2 Schemes with Video-Feature-Dependent Wa-termarks

In [33], a watermarking based video quality assessment metric isproposed based on the research done in [17] ,This scheme is testedunder various distortions, such as MPEG-2 compression, Gaussiannoise addition and Gaussian blur line jittering. The experimentalresults show that with the increasing of the distortion strength, thedistortion caused to the video sequences increase monotonically.Thus, the goal of our research in this paper is to keep the accuracy ofquality estimation achieved in while improving the computationalefficiency and reducing the image quality degradation caused by thewatermark embedding process. Here, the term accuracy evaluatesthe correlation of the estimated quality and the quality calculatedusing the existing objective Full-Reference quality metrics, such asPSNR. The closer the estimated quality to the calculated quality,the more accurate the quality estimation, and vice versa. In thispaper, to propose a new approach that well meets our research goaladdressed above.In the proposed scheme, the adaptive watermark embedding strengthis estimated by analyzing the quality degradation characteristics ofthe cover image and no iterative adjustment loops are used, whichsignificantly improves the computational efficiency and theoreti-cally makes the proposed scheme applicable to real-time video qual-ity estimation. Moreover, the strategies including the HVS maskingare used to guide the watermark embedding process. With the pro-posed scheme, the quality of the watermarked images referring tothe original images is about 48 dB in PSNR on average, which isan 8-dB improvement over the scheme in .The proposed scheme isbased on adaptive watermarking and tree structure in the DWT do-main. Recently, the Set Partitioning in Hierarchical Trees (SPIHT)has become one of the most popular image and video coding methodbecause of its efficiency which is accomplished by exploiting theinherent similarities across the sub bands in the wavelet decom-posed image [15]. The DWT and SPIHT together provide a goodsummarization of local region characteristics of an image which isimportant for adaptive watermark embedding. In this proposedscheme, all the correlated DWT coefficients across the sub bandsare grouped together using the SPIHT tree structure. The DWT

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decomposed image is further decomposed into a set of bit planeimages. In this case, each DWT coefficient is decomposed into asequence of binary bits. The binary watermark bits are embeddedinto the selected Bit planes of the selected DWT coefficients of theselected trees [16]. The HVS masking is used to guide the bit planeselection. As found in the experiments, the higher frequency DWTsub bands and less significant bit planes are more sensitive to dis-tortions and vice versa .Therefore, the robustness of the watermarkis controlled by two factors: (a) The percentages of the watermarkbits embedded into the three DWT levels, respectively, and, (b) Theselection of bit planes for watermark embedding. Thus, for differ-ent selected trees, the watermark embedding strengths are different[19]. The proposed scheme is tested in terms of PSNR, wPSNR,Watson JND and SSIM, and under JPEG compression, JPEG2000compression, Gaussian low-pass filtering and Gaussian noise addi-tion. The results show the effectiveness of the proposed scheme.

Problem Definition

Fig.2 The proposed watermark embedding process

The proposed watermark embedding scheme includes the wa-termark pre-processing, the image pre-analysis, and the watermarkembedding. The watermark embedding process consists of the fol-lowing three steps:Apply 3-level DWT to the original image to obtain the DWT de-composed image. The 3-level DWT decomposed blocks are shown

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in Fig. 2(a).Thus denotations for the 10 DWT decomposed sub-bands will be used throughout the process.Embed the watermark with adaptive embedding strength using thetree structure based watermark embedded. The output of the wa-termark embedded is the watermarked DWT image.Apply 3-level inverse DWT to the watermarked DWT image to ob-tain the watermarked image.

In watermark pre-processing process, a two dimensional originalwatermark is organized column by column into a one dimensionalmatrix. The length of the original watermark sequence is len. Toincrease the probability of the correct watermark bit extractionat the receiver side, every bit in the original watermark sequenceis repeated two times to get a redundant watermark sequence forwatermark embedding [20]. We set Redundancy=3 and the originalwatermark sequence is repeated Redundancy-1 times to get theredundant watermark sequence with Redundancy*len bits long. Inthe image pre-analysis procedure, the texture characteristics andthe perceptual masking effects of the cover image will be analyzedrespectively in the spatial and DWT domains. Based on this imageanalysis, the adaptive watermark embedding strength is estimated[21]. The position separation key is used to locate the positions forwatermark embedding. The Hash table key is an optional inputthat can be used to secure the watermark embedding process

Module Description

1.Spiht Tree Generation And Data Embedding Here we settwo criterias, The watermark bits are not embedded into the LLsub band of the DWT decomposed image The watermark bits arenot embedded into the bit planes higher than 5, where the leastsignificant bit plane is bit plane 1. So we only take 3 dwt bands fordata embedding will called as l=1,2,3 Each dwt bands is convertedto bit planes. The data is embedded in selected bit planes of se-lected dwt coefficients of selected tree. And the bit plane selectionhas great impact on image quality. It is done by HVS maskingtechnique. In the dwt band selection, we cant select all cells of all 3bands. Because after we reconstruct all 4 bands into original imagethe change after watermark embedding will destroy. It will affect

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extracted image quality. If an image of size MxN is 512X512 after3 level dwt decomposition, its size will reduce to 512/8 X 512/8=4096 We avoid LL layer results 4096X(3 / 4)= 3072 trees. Butwe can only use 1 band (different cells of 3 bands). So the ratio isagain 3072 X (1 /3) = 1024 bit plane positions are remaining. Wecan embed watermark+ Redundancy bits into this positions usingEmbed formula is given below.

Cw = c, ifc = ww, ifc 6= w

Fig.2(a) Illustration of the 3-level DWT decomposed sub bands(b) and the formation of tree structure

3 The Selection Of Trees And Dwt Co-

efficients

In this paper, we call the watermark bit assignment as Awb =[a1, a2, a3] where a1, a2, a3 are the number of watermark bits to beembedded in the DWT level 1, 2 and 3 in every selected tree. Inorder to embed watermark, the redundant watermark sequence isdivided intoWsegs segments as depicted in (1).

Wsegs =

[Redundancy ∗ len∑

Awb

]=

[Redundancy ∗ lena1 + a2 + a3

](1)

Where len is the length of the watermark sequence. If we denotethe numbers of rows and columns of the DWT decomposed image

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as M and N, there will be totally Wsegs trees out of M/2N .N/23.3/4trees selected for the watermarking processes.

Fig.3 The tree selection strategy

In Fig.3(b), the X marked positions represent the trees selectedfrom the three DWT orientations for the watermark embedding.The marked positions represent the separation between any twoselected tree positions. In our implementation, we use uniformseparation defined as Equ.(2)

Nsep =

[TNPWsegs

]− 1 (2)

The calculated Nsep will be output as the position separationkey and will be transmitted to the receiver side. Here, we set WsegsTNP . The trees are selected throughout the DWT decomposedimage. The wseg selected trees are further distributed into thethree orientations referring to Tper. In the proposed scheme, we setTper = [1/3, 1/3, 1/3] which means that the trees are evenly selectedfrom the three orientations as illustrated in Fig. 3(a). In this fig-ure, the selected trees are numerically ordered using 1, 2, ...,Wsegs.As presented in Equ. (2), the watermark sequence is divided intoWsegs segments. Thus, the watermark will be embedded into theselected trees segment by segment following the order number ofthe selected tree position

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Fig.4 The tree selection from the three DWT orientations. (a)Graphical illustration. (b) Experimental illustration

After the tree selection, we start to embed the watermark bitsegments into the selected trees referring to Awb. On the l th DWTlevel, the watermark bits are embedded into the DWT coefficientsone by one until the number of is Awb(l) reached, where l=1,2,3.An experimental example of the tree and DWT coefficients selec-tion is shown in Fig. 4 (b). All the selected DWT coefficients aremarked as dark points. The darker points means that the lowersignificant bit planes are selected, and vice versa. In Fig.4 (b), theposition separation key is 3.

4 Security Of The Watermarking Pro-

cess

In the proposed tree structure based scheme, an optional securityfunction is designed to meet some special requirements of users tosecurely transmit signals over internet. This security function iscontrolled by the HashTable Key.

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Fig.5 Illustration of the secure reordering of the selected trees.

The HashTable key is also a position key for the tree selectionand is used to reorder the selected trees. An illustration of the se-cure reordering of the selected trees using the HashTable Key, 0, isshown in Fig.5. In this case, the HashTable Key is also needed tobe sent to the receiver side and the lack of the HashTable key willresult in a failure of watermark extraction.

5 The Analysis of Image Content Com-

plexity

The quad-tree decomposition based complexity analysis is used inthe proposed scheme for a better match with the DWT. For grayscale images, the intensity difference, Vint, is used to verify whetherfurther quad-tree decomposition is needed. Here, we define an in-tensity difference threshold as Tint. If Vint ¿ Tint , the imageor current block will be decomposed into 4 sub-blocks until Vint¡Tint or the size of the sub-block reaches 1. Each quad-tree decom-position is recorded as a decomposition node. The depth of thedecomposition is denoted as the level of decomposition. The con-tent complexity of the cover image is assessed using the followingequation :

Complexity =∑

( i = 1)n(NiX2i)

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where i[1,N] is the current quad-tree decomposition level; n isthe highest decomposition level; Niis the number of quad-tree de-composition nodes on level i . Then, the calculated complexityvalues of all the images in our image library are normalized. In theproposed scheme, the normalized complexity value is used as thecomplexity index which locates in [0, 1].

The image content complexity analysis evaluates many detailsthat an image carries. A higher complexity value indicates that theimage is more complex and the image contains more detail informa-tion [24] & [25]. Comparing to a less complex image, the quality ofa more complex image degrades faster against the same distortion.For this case, to reflect the quality degradation of the cover image,we need to embed more watermark bits into the lower DWT levelsof a more complex image. For a less complex image, we considerto embed more watermark bits into the higher DWT levels. Threeexperimental examples are shown in Fig.6. These quad-tree decom-posed images are achieved using the threshold Tint=0.17 , wherethe maximum intensity value of the cover image is not bigger than1. In this case, the brighter the quad-tree decomposed image, themore complex the cover image. The complexity indices are listedwith the quad-tree decomposed images

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Fig.6:Illustration of the image content complexity analysis.(a),(c)&(e)Original images. (b),(d)&(f) Quad-tree decomposed

images (b):complexity=0.08914 (d):complexity=0.030079 .(f):complexity=0.69012.

6 HVS Masking

The bit plane selection includes two steps: calculating the HVSmasks and mapping the calculated HVS masks to biplane indices[26]. The achieved biplane indices decide which bit planes of theselected DWT coefficients are used to embed the watermark bits.The HVS masking calculation and the HVS-to-bit plane mapping

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are presented in the following two subsections. 1) The HVS Mask-ing: The HVS masking presented in is used in the proposed scheme.Accordingly, four factors greatly affect the behaviour of the HVS:(a) Band sensitivity or frequency masking: Intensity variations areless visible in high resolution sub bands and are also less visible inthe diagonally decomposed blocks,HHl . This factor is expressedusing (3).

MF (1, θ) = M1(θ).M2(1) (3)

where

M1(θ) =

{ √2, if θ = 21, otherwise

M2(1) =

1, if l = 10.32, if l = 20.16, if l = 3

θ =

1, for HL blocks2, for HH blocks1, for LH blocks

(4)

(b) Background luminance: Intensity variations are less visi-ble over the brighter and darker areas. The luminance masking isdenoted as ML.

ML(l, i, j) = 1+I(l, i, j) =

2− 1

256ILL

([i

2,

j

2Le− l

]), ifI(l, i, j) 6= 0.5

1 +1

256ILL

([i

2Le− l ,j

2Le− l

]), otherwise

(5)(c) Spatial masking or edge proximity: The human eyes are

more sensitive to noise addition near edges or contours of images.This factor, ME, is evaluated using the empirically scaled localenergy of the DWT coefficients in all detail sub bands.

MF (l, i, j) =∑ Le−l∑

K=0

ρ

3∑

θ=1

1∑

x=0

1∑

y=0

[Iθk+l(x+ [i

2k], y + [

j

2k])]2 (6)

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where ρis a weighting parameter and the suggested value for ρis presented in the following equation .

ρ =

1

4, if k = 0

1

16k, otherwise

(7)

(d) Texture sensitivity: Intensity variations in highly texturedareas are less visible than those in the flat-field areas of images.This masking factor ,MT is estimated using the local variance ofthe corresponding DWT coefficients in the LL sub band.

MT (l, i, j) = var

{ILL

(x+

[i

2Le− l

], y +

[j

2Le− l

])}(8)

where X=0,1 and y=0,1.The HVS mask is achieved by polling the four factors listed

above and is computed using (5).

MHV S = α.MF .ML.MβE.M

γT (9)

Where MHV S denotes the HVS mask; is a scaling parame-ter.The suggested value for α is 1/2, which implies that intensityvariations having values lower than half of MF .ML.ME.M

γTare as-

sumed invisible. The suggested value for βandγ is 0.2 . In Equation(3) to (8), (i, j) are the coordinates of the current pixel in the cal-culation. I(l.i,j) is the luminance value of the pixel,(i,j) , in onedetail sub band on level l; lε1, 2, .., Le indicates the current DWTlevel in the HVS masking calculation and Le=3 is the maximumlevel applied in the DWT decomposition in this paper;

ILL

([i

2Le−1

],

[j

2Le−1

])is the luminance value of pixel

([i

2Le−1

],

[j

2Le−1

])

in the LL sub band which corresponds to pixel in a detail sub bandon level l.

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Fig.7. Illustration of the generated HVS masks. (a) The originalimage Barbara.(b) The nine HVS masks generated for the nine

DWT detail blocks.

In this work, the binary watermark bits are not embedded in thesub band so that the invisibility of the embedded watermark canbe further improved. Therefore, the HVS masks are only calcu-lated for the nine detail DWT sub bands, with one mask for each.The generated HVS masks for image Barbara is shown in Fig. 7(b).

2) Mapping from HVS Mask to Biplane Indices: To use the HVSmask in the watermark embedding process, a mapping relationshipfrom the coefficients of the HVS mask to the biplane indices isexperimentally defined using the multiple-band threshold method.Considering that different HVS masks have different distributions,the thresholds used in the mapping procedure should be able tochange with the shape of the distribution of the HVS mask [27]. Tofurther limit the quality degradation caused by the watermarkingprocess, only the bit planes from 1 to 5 are used for the watermarkembedding.The thresholds are calculated using (9).

Tn(1, θ) = Sort

([nMmask(1, θ)

5

])(10)

where nε1, 2, 3, 4, Nmask is the total number of the coefficients ofone HVS mask, the denominator indicates that 5 bit planes are usedfor the watermark embedding. The mapping is done using (10).

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Ibp(l, θ, i, j) =

1, v(l, θ, i, j) ≤ T1(l, θ)2, T1(l, θ) < v(l, θ, i, j) ≤ T2(l, θ)3, T2(l, θ) < v(l, θ, i, j) ≤ T3(l, θ)4, T3(l, θ) < v(l, θ, i, j) ≤ T4(l, θ)5, T1(l, θ) < v(l, θ, i, j) ≤ 1

(11)

where(i,j) are the coordinates of a selected DWT coefficient.Ibp(l, , i, j) means the bitplane index achieved for the pixel locatedat (i,j) on DWT level l with orientation θ.v is the value of the HVSmask coefficient.

Fig.7. Illustration of the thresholds selection. (a)&(d): Originalimage Barbara and Treefrog. (b)&(e): Normalized HVS mask ofDWT sub band HL1 of (a)&(d). (c)&(f): Histogram of the mask

shown in (b)&(e).

Therefore, each DWT coefficient in the selected trees has its ownIbp(l, θ, i, j). At the receiver side, the strategy presented above willalso be applied on the distorted image to locate the biplanes for thewatermark extraction [28]. To increase the probability of correctextraction of the watermark bits, all the calculatedIbp(l, θ, i, j) val-ues on the DWT level l at orientation all the calculated Ibp(l, θ, i, j)values are averaged. In other words, in each selected tree, for allthe selected coefficients, the watermark bits will be embedded on

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the same bit plane. Thus, the bit plane indices are updated usingthe following equation:

Ibp(l, θ, itree, jtree) =

[∑(Le−1+1)−1ir=0

∑(Le−1+1)−1jr=0 Ibp(l, θ, i, j)

(2(Le − 1 + 1))2

]

where Ibp(l, θ, itree, jtree) is the averaged bitplane of the DWT coef-ficients located in a specific tree on level l and orientation θ.

i = itree + ir =

[i

2Le−l+1

]+ rem(i− 1, 2Le−l+1)

j = jtree + jr =

[j

2Le−l+1

]+ rem(j − 1, 2Le−l+1)

Two examples of the thresholds selection are shown in Fig.8. Fig.8(b) and (e) are the HVS masks respectively calculated on the DWTsub band HL1 of image Barbara and image Tree frog. Thus, the sizeof these two HVS masks is 1/4 of the image size. Then the HVS-to-bit plane mapping thresholds are calculated using (5). Fig.8(c)and (f) graphically illustrate the threshold selection.The computed thresholds for Fig. 8(c) are[T(1, )T2, T3, T4] = [0.0706, 0.1153, 0.1774, 0.2659].The thresholds for Fig. 8(f) are [T(1, )T2, T3, T4] = [0.1073, 0.1347, 0.1664, 0.2201].With the calculated thresholds, the coefficients of the HVS maskwhich correspond to the selected DWT coefficients are mapped tothe bit plane indices [29]. In this way, the bit planes of the selectedcoefficients for the watermark embedding are located.The Watermark Bits Assignment: With the complexity in-dices, the watermark bits are empirically assigned to the imagesusing the following steps: (a) The complexity indices are dividedinto 6 groups. One integer index is associated with one group.

G =

1, vc > T12, T1 ≥ vc > T23, T2 ≥ vc > T34, T3 ≥ vc > T45, T4 ≥ vc > T56, T5 ≥ vc > 0

Where Gindex is the group index; Vc is the complexity Indext1,t2,t3,t4,t5, and are the empirical grouping thresholds. Thesethresholds may be different for different distortions. With the groupindices, the watermark bits are assigned to the images using

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Recall in Equation (1), trees are selected for the watermark embed-ding, according to the length of the redundant watermark sequence.Thus, for images with different complexities, the number of selectedtrees, Wseg , and the position separation key Nsep, may be differ-ent.

7 Watermark Extraction

The extraction process consists of applying inverse of all the pro-cess. Apply inverse DWT to the received signal. The position sepa-ration key is used to locate the watermarked DWT coefficients [30].The watermark bit assignment is retrieved using the image groupindex transmitted from the sender side. The bit plane indices forwatermark extraction are obtained by calculating the HVS masksof the distorted watermarked image. In one tree, the bit planeindices for all the DWT coefficients on each DWT level are aver-aged. This strategy effectively reduces the watermark extractionerror caused by the bit plane selection in the watermark extractionscheme. Recall that Redundancy=3. The extracted redundant wa-termark sequence is used to recover the three distorted watermarks.Then, the three distorted watermarks are compared bit by bit andthe watermark is extracted using

where We(i,j) , is the extracted watermark bit with coordinates(i,j);N1; is the number of extracted 1s and N0 is the number of extracted0s. Then, the extracted watermark is compared with the original

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watermark bit by bit and the True Detection Rates (TDR) is cal-culated using equation

TDR =Numberofcorrectlydetectedwatermarkbits

Totalnumberofwatermarkbits

The TDR is defined as an extension of the False Detection Rate(FDR) proposed in [5]. The TDR locates in [0, 1] and TDR +FDR = 1. MSE and PSNR are calculated for Quality analysis[31]. With the MSE quality metric, it is assumed that the originalsignal is a perfect signal and any dierence appears in another signalcomparing to the original signal is treated as error [1] &[2]. TheMSE measures the average of the squares of errors. MSE is denedas:

MSE =1

MN

M∑

i=1

N∑

j=1

(I(i, j)− I(i, j))2

where, I and I respectively are the original image and distortedimage; (i, j) are the coordinates of the current pixel in the image;M and N are respectively the numbers of rows and columns of theimage; MN is the total number of pixels in the image. In literature,sometimes, MSE and RMSE are used to evaluate video quality. Inthis case, I and I are the original video frame and the distortedvideo frame. PSNR is a MSE based quality metric and performsquality evaluation by comparing the pixelwise dierences betweenthe distorted image or video frames and the original image or videoframes[3] & [4]. Till now, PSNR is one of the most widely usedquality evaluation metrics. PSNR is dened as:

PSNR = 10 log10

MAX√1

MN

∑Mi=1

∑Nj=1(I(i, j)− I(i, j))2

where, MAX is the maximum possible pixel value of an image orvideo frame. For an 8 bits/pixel grey-scale image, MAX is equalto 255. For a normalized image or video frame, MAX equals to 1Process Flow Diagram

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Fig.9. Processflow of Sender section & Receiver Section Application in Video

Frames

Application in Video Frames The raw video signal can be de-fined as a consecutive sequence of still images. Thus, it is theoreti-cally straightforward to further develop the watermarking based im-age quality evaluation metrics for video quality assessment. Similarto the image quality assessment metrics, the video quality metricscan be classified as the Full-Reference, Reduced-Reference and No-Reference quality metrics [7]. The widely used Full-Reference videoquality metrics include the MSE, PSNR, VQM (Video Quality Met-ric) and VIF (Visual Information Fidelity) [32]. One watermarkcan be redundantly embedded into a number of selected frames ordifferent watermarks can be embedded into different video frameswith the same or different embedding strength(s). In this case,the quality of a watermarked video sequence can be evaluated bycalculating the average degradation of the embedded watermarks.With the watermarking based quality metrics, the quality of thedistorted video signals is usually evaluated in terms of PSNR orMSE [34]. The video quality is usually assessed by comparing thedistorted watermark to the original watermark.

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Result and DiscussionsIn our work, there are 5 test images present in our image library.Besides these image databases, our image library also includes com-puter generated images and more natural images. All of these im-ages are in any size containing different textures, such as, portraits,plants, animals, animations, sceneries, buildings and crowd. Theoriginal watermark used in the experiments is 100x100 in size. Thebinary watermark is randomly generated and there is no specialrequirements for it. The original watermark can be changed to anysize or pattern. Recall that Awb = [a1a2a3] is the watermark bitsassignment which assigns a1bits of watermark to the DWT level 1in a single-root tree; a2bits of watermark to the DWT level 2 ina single-root tree and a3 bits of watermark to the DWT level 3in a single-root tree. The higher the complexity value, the morecomplex the image. The complexity value calculated for the imageJellyfish is 0.69012. The jelly fish, needs a weak watermark embed-ding strength to reflect the degradation caused by the distortion.The TDR value of jelly fish is 0.0521.And the quality factor PSNRis 61.024. The calculated complexity value for image Baboon is0.030079, its TDR value is 0.1018 and PSNR is 58.09. It needs astrong watermark embedding strength. The next image ship hascomplexity-0.016617, PSNR-61.223 and TDR value is 0.0497. ThePSNR values of the entire watermarked image are more than 55.5dB, which proves that the quality degradation caused by the wa-termark embedding process is very limited.

Table. 1. Result Analysis Of various input images

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Conclusion and Future Work

In this paper, a new watermarking based quality estimationscheme is presented. The proposed scheme is designed to estimateimage quality in terms of the existing Full-Reference quality met-rics, such as PSNR, WPSNR, JND and SSIM. Thus, at the receiverside of a communication system, without the original image, thequality of a distorted image or video can still be assessed. Basedon the tree structure, the binary watermark is embedded into the se-lected bit planes of the selected DWT coefficients with adaptive wa-termark embedding strength. The watermark embedding strengthis assigned to an image by pre-analyzing its content complexity inthe spatial domain and the perceptual masking effect of the DWTdecomposed image in the DWT domain. Meanwhile, the watermarkis not embedded in the approximation sub band, which reduces lossin image quality caused by embedding the watermark. The exper-imental results show that the proposed scheme works effectively.In future work, the proposed scheme will be further developed toestimate the quality of an image distorted by multiple distortions.Meanwhile, experiments about image quality estimation in termsof subjective quality scores will be conducted. Since the proposedscheme has good computational efficiency, it is feasible to furtherdevelop the proposed scheme for audio quality evaluation. Thequantization based image quality evaluation scheme has relativelylow computational eciency. The lack of consideration of the hu-man perception characteristics introduces relatively more signicantquality loss caused by the watermark embedding process to thecover images. The quantization based scheme works eectively un-der JPEG compression. The tree structure based image qualityestimation scheme utilizes the SPIHT structure and HVS mappingto guide the watermark embedding process.As the future work, the proposed scheme can be further tested withthe quality evaluation in terms of audio signals. Moreover, the qual-

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ity of image or video signals aected by multiple distortions can alsobe tested using the proposed scheme.

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quality assessment of image and video with adaptive

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