Mathematical Problems in EngineeringVolume 2012 (2012), Article ID 635738, 14 pagesdoi:10.1155/2012/635738

Research Article

Haar-Wavelet-Based Just Noticeable Distortion Model for Transparent Watermark

Lu-Ting Ko,¹ Jwu-E Chen,¹ Hsi-Chin Hsin,² Yaw-Shih Shieh,³ and Tze-Yun Sung³

¹Department of Electrical Engineering, National Central University, Chungli City 320-01, Taiwan²Department of Computer Science and Information Engineering, National United University, Miaoli 360-03, Taiwan³Department of Electronics Engineering, Chung Hua University, Hsinchu City 300-12, Taiwan

Received 3 June 2011; Accepted 5 July 2011

Academic Editor: Carlo Cattani

Copyright © 2012 Lu-Ting Ko et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Watermark transparency is required mainly for copyright protection. Based on the characteristics of human visual system, the just noticeable distortion (JND) can be used to verify the transparency requirement. More specifically, any watermarks whose intensities are less than the JND values of an image can be added without degrading the visual quality. It takes extensive experimentations for an appropriate JND model. Motivated by the texture masking effect and the spatial masking effect, which are key factors of JND, Chou and Li (1995) proposed the well-known full-band JND model for the transparent watermark applications. In this paper, we propose a novel JND model based on discrete wavelet transform. Experimental results show that the performance of the proposed JND model is comparable to that of the full-band JND model. However, it has the advantage of saving a lot of computation time; the speed is about 6 times faster than that of the full-band JND model.

1. Introduction

Watermarking is a process that hides information into a host image for the purpose of copyright protection, integrity checking, or captioning [1–3]. In order to achieve the transparency of watermark, many commonly used techniques are based on the characteristics of human visual system (HVS) [1–13]. Jayant et al. [14, 15] introduced a key concept known as the just noticeable distortion (JND), against which insignificant errors are not perceptible by human eyes. The JND of an image is in general dependent on background luminance, contrast of luminance, and dominant spatial frequency. It takes extensive experimentations to obtain an appropriate JND model.

Perceptual redundancies refer to the details of an image that are not perceivable by human eyes and therefore can be discarded without affecting the visual quality. As noted, human visual perception is sensitive to the contrast of luminance rather than their individual values [16–18]. In addition, the visibility of stimuli can be reduced by nonuniformly quantizing the background luminance [18–20]. The above known as the texture masking effect and the spatial masking effect are key factors that affect the JND of an image. Chou and Li proposed an effective model called the full-band JND model for the transparent watermark applications [21].

Wavelet transform provides an efficient multiresolution representation with various desirable properties such as subband decompositions with orientation selectivity and joint space-spatial frequency localization. In wavelet domain, the higher detailed information of a signal is projected onto the shorter basis function with higher spatial resolution; the lower detailed information is projected onto the larger basis function with higher spectral resolution. This matches the characteristics of HVS. Many wavelet-transform-based algorithms were proposed for various applications [22–34].

In this paper, we propose a wavelet-transform-based JND model for the watermark applications. It has the advantage of saving a lot of computation time. The remainder of the paper proceeds as follows. In Section 2, the full-band JND model is reviewed briefly. In Section 3, the discrete-wavelet-transform- (DWT-) based JND model is proposed. The modified DWT-based JND model and its evaluation are presented in Section 4. Conclusion can be found in Section 5.

2. Review of the Full-Band Just Noticeable Distortion (JND) Model

The full-band JND model [21] makes use of the properties of the HVS to measure the perceptual redundancies of an image. It produces the JND file for image pixels as follows: where where and are the texture mask and the spatial mask, respectively, as mentioned in Section 1, is the average background luminance obtained by using a low-pass filter, , is given in Figure 1, is the maximum gradient obtained by using a set of high-pass filters, , are given in Figure 2, functions and are dependent on the average background luminance, and is the luminance value at pixel position .

Figure 1: Low-pass filter used in (2.6).

(a)

(b)

(c)

(d)

Figure 2: A set of high-pass filters used in  (2.8).

3. Discrete-Wavelet-Transform-Based JND Model

In this section, we propose a novel JND model based on discrete wavelet transform. It has the advantage of reducing computational complexity significantly.

3.1. Discrete Wavelet Transform

Discrete wavelet transform (DWT) provides an efficient multiresolution analysis for signals, Specifically, any finite energy signal can be written by where denotes the resolution index with larger values meaning coarser resolutions, is the translation index, is a mother wavelet, is the corresponding scaling function, , , is the scaling coefficient representing the approximation information of at the coarsest resolution , and is the wavelet coefficient representing the detail information of at resolution . Coefficients and can be obtained from the scaling coefficient at the next finer resolution by using 1-level DWT, which is given by where , , and denotes the inner product. It is noted that and are the corresponding low-pass filter and high-pass filter, respectively. Moreover, can be reconstructed from and by using the inverse DWT, which is given by where and .

For image applications, 2D DWT can be obtained by using the tensor product of 1D DWT. Among wavelets, Haar wavelet [22] is the simplest one, which has been widely used for many applications. The low-pass filter and high-pass filter of Haar wavelet are as follows: Figures 3 and 4 show the row decomposition and the column decomposition using Haar wavelet, respectively. Notice that the column decomposition may follow the row decomposition, or vice versa, in 2D DWT: where , , , and are pixel values, and , , , and denote the approximation, detail information in the horizontal, vertical, and diagonal orientations, respectively, of the input image. Figure 5 shows 1-level, 2D DWT using Haar wavelet.

Figure 3: The row decomposition using Haar wavelet (, , and , are pixel values).

Figure 4: The column decomposition using Haar wavelet (, , , and are pixel values).

Figure 5: 1-level 2D DWT using Haar wavelet (, , , and are pixel values).

The subband of an image can be further decomposed into four subbands: , , , and at the next coarser resolution, which together with , , and forms the 2-level DWT of the input image. Thus, higher level DWT can be obtained by decomposing the approximation subband in the recursive manner.

3.2. Proposed DWT-Based JND Model

As mentioned in Section 2, the full-band JND model [21] consists of (2.1)–(2.8), which is essentially computation consuming. In order to simplify computational complexity, a novel JND model based on Haar wavelet is proposed as follows. where and are the proposed texture mask and spatial mask, respectively, based on DWT, which are given by where is the modified , which replaces the low-pass filter of the full-band JND model. replaces the maximum gradient, , which is given by where, , , and replace , , , and , respectively.

4. Modification of the DWT-Based JND Model

In this section, we introduce an adjustable parameter to modify the DWT-based JND model such that the computation time can be reduced significantly while the performance is comparable to that of the benchmark full-band JND model. The test images, namely, Lena, Cameraman, Baboon, Board, and Peppers are shown in the first row of Figure 12.

4.1. Evaluation of JND Models

Figure 6 shows the distortion-tolerant model, which can be used to evaluate JND models. It takes the JND value as noise, adds to the original image, and computes the peak-signal-to-noise ratio (PSNR) defined as where is the mean squared error, which is defined as where image size is . As shown in Table 1, the proposed DWT-based JND model is somewhat different from the benchmark full-band JND model in terms of the PSNR values.

Table 1: PSNR comparisons of the benchmark full-band JND model, the proposed DWT-based JND model, and the modified DWT-based JND model.

JND model
Lena
Cameraman
Baboon
Board
Peppers

Full-band JND
32.7041
29.9122
34.0845
25.3486
30.3052

DWT-based JND
34.3301
31.7556
35.8156
30.8003
32.1375

The modified DWT-based JND
33.7117
30.9614
34.0931
25.7192
31.7148

Figure 6: Distortion-tolerant evaluation model for the proposed JND model.

4.2. Modified DWT-Based JND Model

Based on (2.1)–(2.3) and (3.6)–(3.8), one can examine the influences of the dominant mask, texture mask, and spatial mask for the full-band JND model and the DWT-based JND model, respectively. Their respective MSE values are shown in Table 2. As one can see, the proposed texture mask for the DWT-based JND model is less significant than that of the full-band JND model. Thus, we propose an adjustable parameter, , to modify (3.17) and (3.18) as follows: where and replace and , respectively. Figures 7, 8, 9, 10, and 11 show the MSE values obtained by modifying the DWT-based texture mask with various in (4.3). In this paper, the adjustable parameter is set to 4 after extensive simulations. The performance of the modified DWT-based JND model with is comparable to that of the full-band JND model in terms of the PSNR and MSE values as shown in Tables 1 and 2, respectively.

Table 2: The MSE values due to the dominant mask (case 1), the spatial mask (case 2), and the texture mask (case 3) using the full-band JND model, the DWT-based JND model, and the modified DWT-based JND model.

JND model
Case
Lena
Cameraman
Baboon
Board
Peppers

Full-band JND
1
34.8876
66.3534
25.3878
189.7653
60.6121

2
28.5202
53.0297
19.6219
41.7222
52.5825

3
10.9574
18.4772
12.373
165.6492
12.583

DWT-based JND
1
23.9924
43.4027
17.0419
54.0822
39.7492

2
23.9269
43.0406
16.942
38.4702
39.642

3
0.972
2.1076
1.4603
24.4885
1.1393

The modified DWT-based JND
1
27.6254
52.1122
25.3378
174.244
43.8131

2
23.9269
43.0406
16.942
38.4702
39.642

3
6.9379
13.7233
14.7008
153.4052
7.8595

Figure 7: MSE values obtained by modifying the DWT-based texture mask using (4.3) with various for Lena image; dashed line: MSE value obtained by using the texture mask of the full-band JND model.

Figure 8: MSE values obtained by modifying the DWT-based texture mask using (4.3) with various for Cameraman image; dashed line: MSE value obtained by using the texture mask of the full-band JND model.

Figure 9: MSE values obtained by modifying the DWT-based texture mask using (4.3) with various for Baboon image; dashed line: MSE value obtained by using the texture mask of the full-band JND model.

Figure 10: MSE values obtained by modifying the DWT-based texture mask using (4.3) with various for Board image; dashed line: MSE value obtained by using the texture mask of the full-band JND model.

Figure 11: MSE values obtained by modifying the DWT-based texture mask using (4.3) with various for Peppers image; dashed line: MSE value obtained by using the texture mask of the full-band JND model.

(a)

(b)

(c)

(d)

Figure 12: (a) The original images, namely, Lena, Cameraman, Baboon, Board, and Peppers; (b), (c), and (d) the noisy images obtained by adding the full-band JND values, the DWT-based JND values, and the modified DWT-based JND values, respectively.

Figure 12 shows the noisy images obtained by adding the JND values to the original images (Figure 12(a)) using the full-band JND model (Figure 12(b)), the DWT-based JND model (Figure 12(c)), and the modified DWT-based JND model (Figure 12(d)). It is noted that the images in the second and fourth rows are almost indistinguishable from the original images. As a result, the modified DWT-based JND model is visually comparable to the full-band JND model.

4.3. Computation Complexity of the Proposed JND Models

In the full-band JND model, the computation of requires 9 multiplications per pixel, the computation of requires 28 multiplications per pixel, and the computation required for (2.2)–(2.5) is 6 multiplications per pixel. Thus, for an image, it requires multiplications. In the proposed DWT-based JND model, the computations of , , , , and require , , (, , and multiplications, respectively, for an image, and the computation required for (3.7)–(3.10) is also 6 multiplications per pixel. Thus, for an image, it requires multiplications. In the modified DWT-based JND model, as the computations of and require no multiplication, it only needs multiplications for an image. Figure 13 shows the log plot of numbers of multiplications required for the three JND models versus different image sizes. As a result, the speed of the DWT-based JND model is about 6 times faster than that of the full-band JND model, which is the main advantage.

Figure 13: Log plot of numbers of multiplications required for the three JND models versus different image sizes.

5. Conclusion

In this paper, an efficient DWT-based JND model is presented. It has the advantage of saving a lot of computation time while the performance is comparable to the benchmark full-band JND model. More specifically, the computation complexity of the proposed DWT-based JND model is only one sixth of that of the full-band JND model. As a result, it is suitable for the real-time applications.

Acknowledgment

The National Science Council of Taiwan, under Grants NSC98-2221-E-216-037 and NSC99-2221-E-239-034, supported this work.

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