An Adaptive Color Image Watermarking Using Wavelet-tree Structure

Yu-Tang Chen , Chung-Yen Su, Yen-Lin Chen Department of Industrial Education,National Taiwan Normal University,Department of Applied Electronics

Technology,National Taiwan Normal University,Department of Applied Electronics Technology,National Taiwan Normal University

60270026H@ntnu.edu.tw

scy@ntnu.edu.tw

698750188@ntnu.edu.tw

Abstract—Digital watermarking is a technique for copyright protection. In literature, many watermarking methods for gray images have been presented. Some of them are directly applied to color images. However, how to choose one of the three components of a color space for watermark embedding is not yet studied. In this paper, we present an adaptive color image watermarking to solve this problem. The proposed method includes an improved wavelet-tree watermarking algorithm and two rules to elevate image quality and watermark robustness. Experimental results show the feasibility of the proposed method. Keywords—Watermarking,wavelet-tree,wavelet transform, copyright protection,colorspace.

I. INTRODUCTION Nowadays, along with the multimedia technology to

flourish, piracy also increases day by day. Therefore, copyright protection is getting more attention. Digital watermarking is such a technique, which embeds some information into a given media. In literature, many watermarking methods for gray images have been presented [1]-[5]. Especially, wavelet-tree based watermarking methods have been made great progress. Previous works show that gray image watermarking problem is how to quantize the wavelet trees to resist common image attacks, such as filtering, compression and cropping. In [5], Run et al. proposed a more effective watermarking scheme than [1]-[4]. They embedded each watermark bit into the maximum and second maximum coefficients of a wavelet tree. However, we found that for some cases their algorithm might suffer a problem that the values of coefficients are over-adjusted and the quality of watermarked images is degraded. In addition, their algorithm might suffer another problem when too many watermark bits “0” are embedded. On the other hand, many watermarking algorithms have been proposed to cope with color images. In [6], Liang and Wang presented an improved wavelet-tree algorithm to embed a binary watermark into the low frequency and medium frequency component of the wavelet decomposition

in the Chrominance Cb domain. In [7], Rawat and Raman presented to embed the gray scale watermark into all the frequency-subbands of the image in each color component of YCbCr space. In [8][9], a binary watermark is embedded into the Y domain while in [10] it is embedded three times in the YCbCr space of images. However, how to choose one of the three components of a color space for embedding a binary watermark once is not yet studied. To solve the aforementioned problems, we present an adaptive color image watermarking based on wavelet-tree structure in this study. The organization of this study is as follows. Section 2 briefly introduces the previous watermark scheme in [5]. Section 3 describes the proposed improved wavelet-tree watermarking algorithm and two rules for choosing a color component. Section 4 shows the experimental results to demonstrate the effectiveness of the proposed method. Finally, conclusions are given in Section 5.

II. PREVIOUS WATERMARK SCHEME

A. Watermark Embedding An image is decomposed into the 4-layer subbands by the

5/3 discrete wavelet transform (DWT). In each layer, the DWT produces four subbands. One is the low-pass band (LL) and the other three are the horizontal band (HL), the vertical band (LH), and the diagonal band (HH), respectively. The LL band is further decomposed into the next layer. We combine one coefficient in the LH4 (or HL4) band and four corresponding coefficients in the LH3 (or HL3) band to be a wavelet tree, as shown in Fig. 1.

1,4C 2,4C

2,3C

3,3C 4,3C1,3C 5,3C 6,3C

7,3C 8,3C

1,4C

2,4C

1,3C 2,3C 3,3C 4,3C

5,3C 6,3C 8,3C7,3C Fig. 1. The wavelet-tree scheme by 4-layer wavelet transform.

This work is partly supported by National Science Council under Contract No. NSC 102-2221-E-003-022.

2014 International Symposium on Computer, Consumer and Control

978-1-4799-5277-9/14 $31.00 © 2014 IEEE

DOI 10.1109/IS3C.2014.136

498

Next, we shuffle all the wavelet trees with a seed and the N-bit watermark with another by a pseudorandom number generator. After that, the first N shuffled trees are used for the watermark embedding. The global average significant difference � of the N-trees is calculated by

� � ������ � ���������

� ������������������������������������������������������� ��� Where �� and ���� denote the local maximum

coefficient and the local second maximum coefficient in the ith wavelet tree, respectively; � � is the floor function.

To improve the robustness of the watermark, a threshold T is defined. Next, the embedding algorithm for each watermark bit is as follows.

Step 1. Adjust the coefficient value of �� to be zero if �� is negative. Step 2. Calculate the significant difference �� of the ith

tree, where �� � ��� ���� . Step 3. If the watermark bit is 1, modify the coefficient

value of �� by

����� ��� �!�������������"#�$%� & '"�(���� )�*+ ������� , -� "#�$%� . '"�(���� )�*�����������������������/0�maxi�"��"/�123�45�213+�� , - 6 7� "#�$%� . '"�(���� )�*�������������������/0����"��"/�128�45�218+

(2)

Where � is an quantization parameter and � is a scale parameter; Maximum(.) denotes the maximum value function.

Step 4. If the watermark bit is 0, modify the coefficient value of �� by

����� � 9�������� � :����"#����� . :+��������������������������4;<�5="��+���� ����������������������������8� After all the watermark bits are embedded, we reshuffle all the wavelet trees with the same seed and a 4-layer inverse DWT is used to generate the watermarked image.

B. Watermark Extraction A watermarked image is decomposed into the 4-layer

subbands by the 5/3 DWT. The wavelet coefficients are grouped to form wavelet trees as the way mentioned in Section 2.1. Next, we shuffle all the wavelet trees by a pseudorandom number generator with the same seed as that in the watermark embedding process. To distinguish an extracted watermark bit, a threshold y is defined by

> � � �� 6 ? � @��6A���

��������������������������������������������������������������������3� Where � is a scale parameter, : . ? B �; � is a sorted

sequence, @ C D@�+ ��" � �E�F� , @� B @G B H B @� , @� � ��I ����I , ��I and ����I denote the local maximum coefficient and the local second maximum coefficient of the ith wavelet tree of the watermarked image, respectively.

For each wavelet tree, the following equation is performed to extract a watermark bit.

JKLMNOKNP�QRL � 9�� "#����I ����I� & >+:� 4;<�5="��+�������������������� ��������������S� Finally, we reshuffle the N-bit watermark by a

pseudorandom number generator with the same seed as that in the watermark embedding process to obtain the binary watermark image.

III. ADAPTIVE COLOR IMAGE WATERMARKING

A. Color Space Conversion And Proposed Rules It is known that RGB color space does not fit human

visual system and is mainly used for computer display. Therefore, we transform a RGB color image into the YCbCr color space. The transformation is as below.

T UVWV5X � T���:YZ[[��� :YS\]��� :Y��3 :Y�^[ :Y88��� :YS::���:YS:: :Y3�[ :Y:\�X T_̀aX��������������������������^�

Next, we use the following two rules for watermark embedding and extraction. Rule 1: To obtain a higher image quality, the watermark is

suggested to be embedded in either Cb or Cr component not in Y component. In addition, it is embedded in the domain with lower standard deviation. In the other words, if the standard deviation of Cb component is smaller than that of Cr component, the watermark is embedded in Cb component; otherwise, it is embedded in Cr component.

Rule 2: To obtain a higher robustness, the watermark is suggested to be embedded into the component with a lower average value of the coefficients in HL4, HL3, LH4, and LH3 subbands.

B. Proposed Watermark Embedding As the algorithm stated in Section 2.1, a negative ��

may be adjusted twice. One is in Step 1, in which it is adjusted to zero. The other is in Step 3 or in Step 4. When the adjustment is in Step 3 and %� . '"�(���� )�, the value of �� is adjusted to generate ����� equal to �� , - or �� , - 6 7 . This results in that the difference between ����� and ���� may be too large. Although large difference leads to a more robust watermark, the price paid is degraded image quality and increased amount of computation. When the adjustment is in Step 4 and ���� is negative, both ����� and ������� will be zero. This also results in degraded image quality and increased amount of computation. To solve this problem, we omit Step 1, and modify Step 3 and Step 4. Specifically, we separately use the following two equations to replace (2) and (3).

����� ���� ��!�� , -����������"# $�� . Maximum��� )�*����������������������/0����45������"�

�������������"/�123�45�213+�� , - 6 7� "# $�� . Maximum��� )�*�������������������������������/0�W4;<����/0����� ���������5��"/�128�45�218+

�]�

499

����� � 9�������� � :����"#��� & :�/0����� . :+����������������������4;<�5="��+����������������������������� ���\� In (7), the condition of ���� is also considered. In (8),

both the values of �� and ���� are not adjusted to zero when both of them are negative. With the proposed method, a negative �� is adjusted only once, which results in a faster execution of watermark embedding than the previous method. In addition, the difference between ����� and ���� will not be adjusted too large, which results in an improved image quality.

C. Proposed Watermark Extraction The proposed watermark extraction is almost the same as

that in the previous watermark extraction process. But their thresholds of y are different. In the previous watermark extraction process, a threshold y is needed to calculate. However, we found that the value of y will be too small when the number of watermark bit 0 is too large. This leads to that the number of erroneous watermark bit increases when attacks occur. To solve this problem, we use the following equation to replace (4). > � @b�������������������������������������������������������������������������������������������[�

Where b is the number of watermark bit 0. Since the threshold y is exactly equal to the bth � value of the sorted sequence, the computation is also reduced.

IV. EXPERIMENTAL RESULTS We use the peak-signal to noise ratio (PSNR) and the

normalized correlation coefficient (NC) to evaluate the image quality and watermark robustness. The PSNR of the color image is defined as below.

cdef � �: g4h�i ZSSG�j k'lm�_� , 'lm�`� , 'lm�a�n � ��:� Where MSE is the mean square error of the watermarked

image. The NC is defined as follow.

eo � �=p 6 =�� � =�"� q� 6 =I�"� q����rs�t�i

�us���i ������������

Where =p and =� respectively represent the height and width of watermark, = and =I respectively represent the bit value of the original watermark and the extracted watermark.

We use 13 RGB color images, as shown in Fig. 2, for the experiment. Each image has the size 512 × 512 and 24 bits per pixel. In addition, we use two types of watermarks with the same size 32 × 16. One is named as N1, which consists of 363 white points (watermark bit 1) and 149 black points (watermark bit 0). The other is named as N2 including 149 white points and 363 black points.

We use the same parameters as those in [5] to let T = 10, � = 7, and � = 1.5. Fig. 2 shows the PSNR comparison. As shown in Fig. 2, the proposed algorithm always produces higher PSNR than [5]. The value of PSNR is increased about 0.21 dB on average. Since Cb and Cr components are lesser sensitive to visual senses than Y component, their PSNR values are higher than those of Y component. Fig. 3 shows the NC comparison results for Lena image after attacks by a Gaussian filter, average filters (3×3, 5×5, 7×7), and median filters (3×3, 5×5, 7×7). In the figure, Gau.+Avg.NC is the average of NC values obtained by the Gaussian filter and three average filters, while Med.NC is the average of NC values obtained by three median filters. As we can see in Fig. 3, most of the NC values of the proposed method are larger than 0.9, and are higher than those obtained by the method of [5]. Especially, their difference becomes large when the watermark N2 is used. This is because the proposed method provides a better threshold to extract the watermark bits.

TABLE I

AVERAGE VALUE OF THE SUBBAND COEFFICIENTS AND THE STANDARD DEVIATION IN CR AND CB COMPONENTS FOR LENA AND JET IMAGES.

Image Component Ave. Std.

Lena Cr Cb

2.5551 2.5511

12.8040 13.6379

Jet Cr Cb

2.0851 3.0956

9.5919 11.2300

TABLE II III

NC VALUES OF LENA AND JET IMAGES.

Image Watermark Comp. Gau.+Avg.NC Med.NC

Lena N1 Cr

Cb 0.9717 0.9737

0.9610 0.9610

N2 Cr Cb

0.9727 0.9688

0.9505 0.9518

Jet N1 Cr

Cb 0.9873 0.9756

0.9740 0.9610

N2 Cr Cb

0.9795 0.9678

0.9675 0.9388

To show the feasibility of Rule 2, we also tabulate the

average values of the subband coefficients of Lena and Jet images in Table 1. As can be seen in Table 1, the Cr component of Jet image has smaller average value than the Cb component (their difference is obvious). Therefore, the watermark is suggested to be embedded into the Cr component. The NC results of Jet image in Table 2 confirm the effectiveness of Rule 2 no matter which watermark is embedded.

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Fig. 2. The PSNR of 13 color watermarked images for embedding watermark (a) N1 and (b) N2

Fig. 3. Comparison the NC values of Lena image for different methods.

Only the NC values in Cb domain are shown. (a) the watermark is N1 (b) the watermark is N2.

V. CONCLUSION This paper presented an adaptive color image

watermarking for copyright protection. The presented algorithm includes an improved wavelet-tree watermarking scheme and two rules for adaptively choosing a color component for watermark embedding. The proposed algorithm not only can produce higher image quality than the previous methods but also can maintain the robustness of the watermark. Experimental results show the feasibility of the proposed algorithm.

REFERENCES [1] S.H. Wang and Y.P. Lin, “Wavelet tree quantization for copyright

protection watermarking,” IEEE Transactions on Image Processing, vol.13, pp. 154-165, 2004.

[2] B.K. Lien and W.H. Lin, “A watermarking method based on maximum distance wavelet tree quantization,” in the Proc. the 19th conference computer vision, graphics and image processing, 2006.

[3] W.H. Lin, S.J. Horng, T.W. Kao, P.Z. Fan, C.L. Lee, and Y. Pan, “An efficient watermarking method based on significant difference of wavelet coefficient quantization,” IEEE Transactions on Multimedia, vol.10, pp. 746-757, 2008.

[4] W.H. Lin, Y.R. Wang, and S.J. Horng, “A wavelet-tree-based watermarking method using distance vector of binary cluster,” Expert Systems with Applications, vol.36, pp. 9869-9878, 2009.

[5] R.S. Run, S.J. Horng, W.H. Lin, T.W. Kao, and P. Fan, “An efficient wavelet-tree-based watermarking method,” Expert Systems with Applications, vol.38, pp. 14357-14366, 2011.

[6] F. Liang and L. Wang, “An improved wavelet-based color image watermark algorithm,” Journal of Computational Information Systems, vol.6, pp. 2013-2020, 2011.

[7] S. Rawat and B. Raman, “A new robust watermarking scheme for color images,” in the Proc. IEEE 2nd International Advance Computing Conference, pp. 206-209, 2010.

[8] Q. Su, X. Liu, and W. Yang, “A watermarking algorithm for color image based on YIQ color space and integer wavelet transform,” in the Proc. International Conference on Image Analysis and Signal Processing, pp. 70-73, 2009.

[9] S. Hongqin and L. Fangliamg, “A blind digital watermark technique for color image based on integer wavelet transform,” in the Proc. International Conference on Biomedical Engineering and Computer Science, pp. 1-4, 2010.

[10] Y. Li, Y. Hao, and C. Wang, “A research on the robust digital watermark of color radar images,” in the Proc. IEEE International Conference on Information and Automation, pp. 1091-1096, 2010.

38 40 42 44 46 48

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Lena Baboon Jet Pepper Boat Splash Tiffany House Kodak1 Kodak2 Kodak3 Kodak4 Kodak5

PSN

R (d

B) (a)

YCrCb

36 38 40 42 44 46 48 50

[5] Pro. [5] Pro. [5] Pro. [5] Pro. [5] Pro. [5] Pro. [5] Pro. [5] Pro. [5] Pro. [5] Pro. [5] Pro. [5] Pro. [5] Pro.

Lena Baboon Jet Pepper Boat Splash Tiffany House Kodak1 Kodak2 Kodak3 Kodak4 Kodak5

PSN

R (d

B) (b)

YCrCb

0.75 0.80 0.85 0.90 0.95 1.00

NC

(a)�= 0.6 �= 0.7 �= 0.8 �= 0.9 �= 1.0 Pro.

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

NC

(b)�= 0.6 �= 0.7 �= 0.8 �= 0.9 �= 1.0 Pro.

501