ABSTRACT A novel visible watermarking algorithm based on noise reduction and Human Visible System (HVS) model approach is presented in this study. In order to get the best tradeoff between the embedding energy of watermark and the perceptual translucence for visible watermark, the composite coefficients using global and local characteristics of the host image in the discrete wavelet transform (DWT) domain is considered. The application of the perceptual model of contrast-sensitive function (CSF) with the noise reduction of the visibility thresholds for HVS in DWT domain achieves the goal of fine tuning of the perceptual weights according to the basis function amplitudes for the best quality of perceptual translucence. Instead of three types of block classification-textures, edges and smooth areas, the computation of Noise Visibility Function (NVF) characterizes the local image properties to determine the optimal watermark locations and strength at the watermark embedding stage. The experimental results demonstrate that the proposed technique improves the PSNR values and visual quality than the CSF only based algorithms

Chih-Wen Lin

Min-Jen Tsai

CiteSeerX

The Collaboration of Noise Reduction and Human Vision System Models for a Visible 
Watermarking Algorithm *
Min-Jen Tsai and Chih-Wen Lin
Institute of Information Management, National Chiao Tung University,  R.O.C. 
 
ABSTRACT 
A novel visible watermarking algorithm based on noise reduction 
and Human Visible System (HVS) model approach is presented in 
this study. In order to get the best tradeoff between the embedding 
energy of watermark and the perceptual translucence for visible 
watermark, the composite coefficients using global and local 
characteristics of the host image in the discrete wavelet transform 
(DWT) domain is considered. The application of the perceptual 
model of contrast-sensitive function (CSF) with the noise 
reduction of the visibility thresholds for HVS in DWT domain 
achieves the goal of fine tuning of the perceptual weights 
according to the basis function amplitudes for the best quality of 
perceptual translucence. Instead of three types of block 
classification- textures, edges and smooth areas, the computation 
of Noise Visibility Function (NVF) characterizes the local image 
properties to determine the optimal watermark locations and 
strength at the watermark embedding stage. The experimental 
results demonstrate that the proposed technique improves the 
PSNR values and visual quality than the CSF only based 
algorithms. 
Index Terms—Visible watermarking, HVS, CSF, NVF. 
1. INTRODUCTION 
Because of the advantages of digital media and rapid development 
of digital signal processing, a variety of multimedia contents have 
been digitalized and easily distributed or duplicated without any 
reduction in quality through both authorized and unauthorized 
distribution channels. With the ease of editing and reproduction, 
protection of the intellectual rights and authentication of digital 
multimedia becomes an important topic in these years. 
Digital watermarking [1] has been extensively studied and 
regarded as a potentially effective means for copyright protection 
recently. Among different categories of researches for digital 
watermarking,  visible watermarking protect copyrights in a more 
active way since the approach not only prevents pirates but also 
visually recognizes the copyright of multimedia data.  
Digital contents embedded with visible watermarks will overlay 
recognizable but unobtrusive copyright patterns identifying its 
ownership. Therefore, a visible watermarking technique should 
remain details of contents and ensure embedded patterns difficult 
or even impossible to be removed, and no one could use 
watermarked data illegally. 
From the literature survey, visible watermarking has captured 
less attention than invisible one. Several visible watermarking 
schemes [2-4] have been proposed in transform domain such as 
DCT and DWT. Huang and Tang [2] presented a contrast sensitive 
visible watermarking scheme with the assistance of HVS. They 
first compute the CSF mask of the discrete wavelet transform 
domain. Secondary, they use square function to determine the 
mask weights for each subband. Third, they adjust the scaling and 
embedding factors based on the block classification with the 
texture sensitivity of the HVS. However, their scheme doesn’t 
consider the following issues: 
1. The application of CSF for the HVS in the wavelet transform 
domain 
2. The embedding factors emphasize more weights in the low 
and high frequency domain instead of the medium-to-high 
frequency domain. 
3. The interrelationship of block classification and the location 
characteristics 
For the first issue, the direct application of CSF for the HVS in the 
wavelet transform domain needs to be further studied [4-6].  For 
the second issue, the watermark embedding in the low frequency 
components results high degradation of the image fidelity. In 
addition, the high frequency components of the watermarked 
image easily suffer common image signal processing attacks with 
low robustness. For the third issue, the block classification in [2] 
should be further considered for the local and global characteristics 
in DWT domain.  
The goal of this paper is to present a novel visible watermarking 
algorithm based on noise reduction and HVS model  approach. The 
collaboration of CSF and NVF for HVS models is leveraged with 
the noise reduction of the visibility thresholds for HVS in DWT 
domain. The perceptual weights is fine tuned for watermark 
embedding which results significant improvement over the 
watermarked images by CSF only algorithms regarding the image 
quality, translucence and robustness of the watermarking. 
This paper will be organized as follows. The details of the 
algorithm will be explained in Section 2. Section 3 will show the 
experiments with discussion and conclusion is in Section 4. 
2. THE APPROACH AND THE WATERMARKING 
ALGORITHM 
HVS research offers the mathematical models about how humans 
see the world. Psychovisual studies have shown that human vision 
has different sensitivity from various spatial frequencies 
(frequency subbands). The HVS by using the contrast sensitive 
function (CSF) and noise visibility function (NVF) is integrated in 
this study and will be explained in brief as following: 
___________________________________________________  
* This work was supported by the National Science Council in 
Taiwan, Republic of China, under NSC94-2416-H009-018 
and NSC95-2416-H009-027. 
III - 2731-4244-1437-7/07/$20.00 ©2007 IEEE ICIP 2007
CSF (Contrast Sensitive Function) 
Mannos and Sakrison [3] originally presented a model of the 
CSF for luminance (or grayscale) images is given as follows: 
1.1)*114.0(*)*114.00192.0(*6.2)( feffH where 22 yx fff  
is the spatial frequency in cycles/degree of visual angle (fx and fy 
are the spatial frequencies in the horizontal and vertical directions, 
respectively). The HVS is most sensitive to normalized spatial 
frequencies between 0.025 and 0.125 and less sensitive to low and 
high frequencies.  
CSF masking [4-5] is one way to apply the CSF in the discrete 
wavelet domain. CSF masking refers to the method of weighting 
the wavelet coefficients relative to their perceptual importance. 
Some well-designed CSF masks which transforms the CSF curve 
into perceptual importance weight are presented in [5] and [2] uses 
the same method in [5] for 11-weight DWT CSF mask. For a 5 
level pyramidal DWT decomposition, the HVS is most sensitive to 
the distortion in mid-frequency regions (level 3) and sensitivity 
falls off as the frequency value drifts on both sides (level 1, 2, 4 
and 5). The square function in [2] is applied to approximate the 
effect of CSF masking. The adequate modulation rate k for each 
subband is determined by:
2
2
20.7
)20.7(01.0
k
k r , rk represents 
the wavelet coefficient CSF of the perceptual importance weight 
for each subband where k denotes the decomposition level.  
NVF (Noise Visibility Function) 
   S.Voloshynovskiy et al. [7] presented a stochastic approach 
based on the computation of a NVF (Noise Visibility Function) 
that characterizes the local image properties and identifies texture 
and edge regions. This allows us to determine the optimal 
watermark locations and strength for the watermark embedding 
stage. The adaptive scheme based on NVF calculated from 
stationary GG model is superior to other schemes, which is defined 
as follows: 
(1)2
( , )( , )
( , ) I
w x yNVF x y
w x y
 
 , 
2),(
1)]([),(
yxr
yxw  and 
2
I  is the global variance of 
the cover image I, )/1(/)/3()( , 
0
1)( duues su  
(gamma function) and 
I
yxIyxIyxr ),(),(),( .  is the shape 
parameter and r(x,y) is determined by the local mean and the local 
variance. For most of real images, the shape parameter is in the 
range 0.3 1.  
  In order to further improve the HVS model for better image 
quality, the knowledge of detection thresholds for DWT 
coefficients should be also studied. A.B. Watson, et al. [6] 
proposed a mathematical model for DWT noise detection 
thresholds which is a function of level, orientation, and display 
visual resolution. The model is given by 
2
0
log log (log log )Y a K f g f  
where a  is the minimum threshold occurs at spatial frequency 
0
g f , f  is the spatial frequency of decomposition level , and g  
shifts the minimum by an amount that is a function of orientation. 
Table I shows the basis function amplitudes for a 5-level DWT. In 
this study, we use ,A  indicating the basis function amplitudes,  
as DWT level, and  as orientation. 
From above discussion, we have implemented the CSF based 
visible watermarking and found the direct application of CSF 
square function in [2] emphasizes more weights in the low and 
high DWT frequency domain. Subsequently, the high frequency 
components of the watermarked image easily suffer common 
image signal processing attacks. In addition, this approach affects 
the quality of watermarked images and their PSNR values are 
often below 30dB for 512x512 color images. According to this 
observation, the concept of DWT noise detection threshold is 
adopted here to fine tune the perceptual weights by the basis 
function amplitudes ,A  from [6]. Therefore, the perceptual 
weighting is modified as following: 
2
2, , , ,
, ,
(7.20 )
0.4 0.2, 0.2
7.20
1 (2)
r
A if
 
Here, , and , are scaling and embedding factors,  is the 
DWT level, and  is orientation where  is the wavelet 
coefficient CSF of the perceptual importance weight as shown in 
Fig. 1. Meanwhile, , and ,  are the global characteristics of 
the host image, and they are independent to the digital images. 
TABLE I 
BASIS FUNCTION AMPLITUDES FOR A FIVE-LEVEL 9/7 DWT
Level Orien- 
tation 1 2 3 4 5 
LL 0.62171 0.34537 0.18004 0.09140 0.045943
HL 0.67234 0.41317 0.22726 0.11792 0.059758
LH 0.67234 0.41317 0.22726 0.11792 0.059758
HH 0.72709 0.49428 0.28688 0.15214 0.077727
Fig. 1. A five-level wavelet pyramidal decomposition.  
r ( , ) values for each level  are indicated at 
the center of each band. 
 
III - 274
To further improve the application of block classification by 
simply categorizing three type blocks in [2], the local and global 
characteristics in DWT domain is considered. In our content 
adaptive scheme, a stochastic image model for watermark 
embedding is adopted by using the NVF which characterizes the 
local image properties. In our scheme, we use the stationary GG 
model in the embedding stage, and the estimated shape parameter 
for 0.6 , and width of window is 1. In summary, the complete 
design of the proposed algorithm is summarized as following and 
the flow chart is shown in Fig. 2: 
Watermarking embedding algorithm: 
(1) The host color image is converted in the color space domain 
from RGB to YCrCb. 
(2) By using Bi9/7 filter from [6], compute the wavelet 
coefficients of Y component from host color image and 
grayscale watermark image. If the width of watermark is not 
the same as the one of the host image, it should be 
proportionally scaled to the host image.  
(3) Modify the DWT coefficients of the host image by using the 
following equation  
, , , ,, ,(1 )i j i j i j i jY X N VF S  
Note: (i, j) indicates the spatial location. X and S are the 
decomposed wavelet coefficients of the original image and 
the watermark. NVF is defined in formula (1) and the 
relationship of , and ,  is defined in (2).  
(4) Inverse transform the DWT coefficients of the host image to 
obtain a watermarked image. 
 
3. EXPERIMENTS AND DISCUSSION 
The proposed visible watermarking algorithm has been 
implemented and intensively tested by using the commonly 
available color images from USC image database [8] and the 
performance of 512x512 color Lena, F16, Baboon, Peppers, Splash 
and Tiffany  images are tabulated for comparison in Table II. The 
grayscale watermark of logo image adopted in the experiments is a 
NCTU school logo and shown in Fig. 3.  
Fig. 4 (a) and 4 (d) illustrate the original cover images of Lena 
and F16 from [8], the results watermarked images from [2] are 
compared with the proposed approach and the results are in Fig. 4 
(b)(c) and (e)(f). The performance analysis can be categorized as 
following: 
A. Visual Quality 
From Fig. 4 (b)(c) and (e)(f), the proposed method has the closest 
luminance maintenance compared with the original ones even the 
difference is sometimes identified subjectively. The watermarked 
images by using [2] have more bright effect in the unmarked areas. 
To further compare the details from the watermarked images, Fig. 
5(a) and 5(d) are the close-up of the original image.  Fig. 5(b) and 
5(e) are the close-up of Fig. 4(b) and 4(e) by using [2]’s method.  
Fig. 5(c) and 5(f) are the close-up of Fig. 4(c) and 4(f) by using our 
proposed scheme. It is very clear that the watermark’s edges and 
thin lines are blurred in Fig. 5(b) and 5(e) but the watermark 
patterns in Fig. 5(c) and 5(f) still has sharp edge and the logo 
watermark is evidently embedded. 
 
B. PSNR (peak signal-to-noise ratios) 
To make a fair comparison with the method from [2], it is better 
to embed the same watermark for the same cover image. However, 
the watermark used in [2] is not available currently, we embed the 
logo watermark from Fig. 3 to make the best effort for 
performance comparison. The tabulated results from TABLE II 
disclose that our watermarking scheme can achieve higher PSNR 
values than the method in [2] where the PSNRs are generally 
below 30dB for different images. The low PSNRs have positive 
correlation with the degradation in image quality. This denotes the 
fidelity of images from our method is better than the CSF only 
based method. 
 
TABLE II 
PSNR SUMMARY OF WATERMARKED COLOR IMAGES
Image  Method of [7] 
Proposed 
method 
Lena 27.1 dB 32.1 dB 
Baboon 27.2 dB 30.5 dB 
F16 28.9 dB 31.7 dB 
Peppers 26.9 dB 31.8 dB 
Splash 27.2 dB 31.6 dB 
Tiffany 28.1 dB 32.4 dB 
    
Fig.  3. Tested grayscale watermark : NCTU logo  
+
 
Fig. 2. The flow chart of the proposed visible watermarking 
approach 
 
III - 275
C. Median Filtering 
StirMark [9] software is adopted here for this attack. Fig. 6(a) is 
close-up of original Baboon image. Fig. 6(b) is close-up of 7x7 
median filtering of watermarked image by the method of [2]. Fig. 
6(c) is close-up of 7x7 median filtering of watermarked image by  
the proposed method. It is apparent that the logo pattern (i.e. the 
characters of E, S, A) is still evidently existed in Fig. 6 (c) but is 
blurred and hard to be recognized in Fig. 6 (b). 
Other attacks from [9] are also preformed and the experimental 
results are consistent with the above findings which indicate our 
visible watermarking scheme has better visual effect and high 
PSNR values than other schemes like [2]. In summary, an 
intensive comparison for proposed technique has been illustrated 
above. Different attack and visual quality comparison is also 
illustrated. Therefore, we can conclude that the proposed method is 
more robust with better image quality than the algorithm in [2].  
4. CONCLUSION 
We propose a new visible watermarking technique where the 
intensity of the watermark in different regions of the image 
depends on the underlying content of the image and humans’ 
sensitivity to spatial frequencies. The collaboration of CSF and 
NVF for HVS models is leveraged with the noise reduction of the 
visibility thresholds for HVS in DWT domain. The perceptual 
weights is fine tuned for watermark embedding which results 
significant improvement over the watermarked images by CSF 
only based algorithms regarding the image quality, translucence 
and robustness of the watermarking. The experimental results 
demonstrate the proposed visible watermarking scheme has 
achieved high PSNR values with better visual fidelity and 
robustness to attacks than other schemes. 
REFERENCES 
[1] I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon,, “Secure spread 
spectrum watermarking for multimedia”, IEEE Trans. on Image 
Proc., vol. 6, no. 12, pp. 1673-1687, Dec. 1997. 
[2] B. B. Huang and S. X. Tang, “A contrast-sensitive visible 
watermarking scheme”, IEEE Multimedia, vol. 13, no. 2, pp. 60-66, 
April-June 2006. 
[3] J. L. Mannos and D. J. Sakrison, “The effects of a visual fidelity 
criterion on the encoding of images”, IEEE Trans. on Info. Theory, 
vol. 20, no. 4, pp. 525-536, Jul. 1974. 
[4] D. Levick   and P. Fori s, “Human Visual System Models in Digital 
Image Watermarking,” RADIOENGINEERING, vol.13, no. 4, pp. 
38-43, 2004. 
[5] A.P. Beegan, L.R. Iyer, and A.E. Bell, “Design and Evaluation of 
Perceptual Masks for Wavelet Image Compression”,  Proc. 10th 
IEEE Digital Signal Processing Workshop, IEEE CS Press, pp. 88-
93, 2002. 
[6] A.B. Watson, G.Y. Yang, J.A. Solomon, J. Villasenor,” Visibility of 
wavelet quantization noise”, Image Processing, IEEE Transactions 
on, vol.6, no.8, pp. 1164-1175, 1997. 
[7] S. Voloshynovskiy, et al., “A stochastic approach to content 
adaptive digital image watermarking”, in Proc. 3rd Int. Workshop 
Information Hiding, Dresden, Germany, pp. 211-236, Sep. 1999. 
[8] USC SIPI–The USC-SIPI Image Database 
[Online]:http://sipi.usc.edu/services/database/Database.html 
[9] StirMark,http://www.petitcolas.net/fabien/software/StirMarkBench
mark_4_0_129.zip 
  
(a)                                (b) 
Fig.  3. (a) NCTU logo and (b) IIM logo images 
   
              (a)          (b)                                 (c) 
   
(d)                               (e)                                 (f) 
   Figure  4. Original Lena (a) and original F16 (d).  Watermarked 
Lena (b)   and watermarked F16 (e) from Huang and Tang’s  
method. Watermarked Lena (c) and watermarked F16 (f) from our 
method. 
 
   
              (a)          (b)                                 (c) 
   
(d)                                  (e)                                  (f) 
Figure 5. Close-ups of figure 4 for comparison.  
 
   
               (a)          (b)                                 (c) 
Figure 6. Median filtering (7×7) attacks to two methods for 
comparison. 
 
 
   
              (a)         (b)            (c) 
    
(d)                             (e)                             (f) 
   Fig.  4. The visual quality comparison of original and 
watermarked images. (a) original Lena image. (b) 
watermarked Lena image by the method from [2] (c) 
watermarked Lena image by the proposed algorithm  
(d) original F16 image (e) watermarked F16 image by 
the method from [2] (f) watermarked F16 image by 
the proposed algorithm  
 
   
              (a)        (b)                             (c) 
    
(d)                             (e)                              (f) 
Fig.  5. The visual quality comparison of close-ups for images in 
Fig. (4) (a) original Lena image. (b) watermarked Lena 
image by the method from [2] (c) watermarked Lena 
image by the proposed algorithm  
(d) original F16 image (e) watermarked F16 image by 
the method from [2] (f) watermarked F16 image by the 
proposed algorithm  
 
   
               (a)        (b)                              (c) 
Fig.  6. The visual quality comparison of close-ups for Baboon 
image (a) original Baboon image (b) watermarked 
Baboon image by the method from [2] (c) watermarked 
Baboon image by the proposed algorithm  
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The collaboration of noise reduction and human vision system models for a visible watermarking algorithm

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The collaboration of noise reduction and human vision system models for a visible watermarking algorithm

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