Journal of Computational Information Systems 6:8(2010) 2675-2682 Available at http://www.Jofcis.com
Energy Quantization Modulation Approach for Image Watermarking Hong PENG1,†, Jun WANG2, Zulin ZHANG3, Honghong CHEN1, Xuezhen ZHANG1 1 2 3
School of Mathematics and Computer Engineering, Xihua University, Chengdu, Sichuan 610039, China School of Electrical and Information Engineering, Xihua University, Chengdu, Sichuan 610039, China
Department of Computer Science, Sichuan University of Nationalities, Kangding, Sichuan 626001, China
Abstract For copyright protection of digital image, we present an energy quantization modulation approach for digital image watermarking in this paper. First, we introduce the concept of local energy index of a coefficient block in approximation sub-band of an image. Second, an energy quantization modulation technique is developed to modulate the local energy index of each coefficient block. Moreover, in order to realize the energy quantization modulation technique, we employ a coefficient modification strategy that proportions the modification amount, and develop corresponding coefficient modulation rules. The energy quantization modulation approach not only can ensure good imperceptibility, but also can effectively improve the robustness against attacks. The proposed watermarking algorithm has simple computation complexity, and can effectively reduce image’s distortion. Performance of the proposed approach is evaluated under JPEG compression, additive noise, filtering, etc. It is shown that the proposed energy quantization modulation technique is superior to conventional quantization-based techniques. Keywords: Digital Watermarking; Image Watermarking; Local Energy Index; Energy Quantization Modulation
1. Introduction Digital watermarking technique provides efficient tools for protecting the intellectual property rights of multimedia data [1]. The quantization-based methods are a kind of important watermarking techniques. The main idea is that original host data is quantized into different quantization intervals according to different watermark information, and we extract watermark bits by adjudging the quantization interval in which the data falls during watermark extraction. Chen et al [2] proposed a class of watermarking methods of quantization index modulation, which had universal significance. Chou et al [3] proposed a quantization-based color image watermarking algorithm, which embedded color watermark into a color image. In most of dither modulation methods, the modulated objects are the amplitude or phase of the coefficients in transform domain [4]. In Miyazaki [5], a dither modulation algorithm in DCT domain was developed. Kundur et al [6] proposed a watermarking method, which embedded binary watermark by modifying amplitude relationship of three wavelet coefficient from distinct detail sub-bands of same resolution level of host image, and the middle coefficient was quantized to encode either zero or one bit. In Me [7], they presented a quantization-based watermarking methods based on dither modulation to middle frequency coefficients in DWT domain. Chen et al [8] proposed a mean quantization-based image †
Corresponding author. Email addresses:
[email protected] (Hong PENG),
[email protected] (Jun WANG).
1553-9105/ Copyright © 2010 Binary Information Press August, 2010
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watermarking method. Inoue et al [9] proposed a digital watermarking method based on mean dither quantization for video data. Ejima et al [10] presented a mean quantization-based watermarking method for still images by using wavelet packets. Chae et al [11] proposed a robust data hiding technique using multidimensional lattices quantizer. Chou et al. presented an adaptive quantization watermarking method based on a color visual model [12]. Masry et al proposed an adaptive quantization method using psychovisual model of color image [13]. Hu et al [14] proposed an adaptive quantization watermarking method, in which the JND value was used as adaptive quantization size. Paquet et al proposed optimal mean quantization method [16]. Wu et al [17] proposed a novel digital image watermarking scheme based on vector quantization technique. Koval et al [18] proposed a quantization-based watermarking method, which underwent additive white Gaussian noise (AWGN) and uniform noise attacks. In this paper, we propose an energy quantization modulation approach for robust image watermarking. Firstly, we divide approximation sub-band of an image into coefficient blocks, and then define the concept of local energy index of coefficient block. Secondly, based on the local energy index, we develop an energy quantization modulation technique, which embeds a watermark bit into a coefficient block by modulating its local energy index. In order to realize the modulation technique, we develop corresponding coefficient modulation rules by using the modification strategy that proportions the modification amount. The main contribution of this paper is to develop an energy quantization modulation technique and present corresponding robust image watermarking approach. The advantages of the proposed watermarking approach are as follows. (i) Since the energy quantization modulation technique modulates watermark bit into a set of wavelet coefficients in each coefficient block of approximation sub-band of an image, the proposed watermarking approach can reduce effectively image distortion. Therefore, the modulation technique is more robust to resist signal processing or attacks. (ii) Due to coefficient modification strategy that proportions the modification amount, the proposed watermarking approach can ensure good imperceptibility of watermarking system. (iii) The proposed watermarking algorithm has lower computation cost due to its simple realization. 2. Principle of Energy Quantization Modulation Suppose I is a grey image with size M1× M2. For image I, we can obtain its four sub-bands by performing wavelet decomposition with one level, shown in Fig.1, where LL1 is its low-frequency sub-band with size (M1/2)×(M2/2). According to previous knowledge, we can know that sub-band LL1 concentrates most of energy of the image. In this paper, we plan to embed watermark by modulating DWT coefficients in sub-band LL1. In order to realize our energy quantization modulation, we need accomplish block-wise operation for the sub-band LL1. Thus, the sub-band LL1 is divided into non-overlapping coefficient blocks with size m1×m2, shown in Fig.2, where each coefficient block contains (m1×m2) DWT coefficients. In Fig.2, m1=m2=4. Formally, we denote these coefficient blocks as C = {Cuv u = 1, 2, … ,[ M 1 (2 × m1 )], v = 1, 2,… ,[ M 2 (2 × m2 )]} , Cuv = {cuv (i, j ) i = 1,… , m1 , j = 1, … , m2 }
(1)
where Cuv expresses the (u,v)-th coefficient block in low-frequency sub-band LL1, and cuv(i,j) is its (i,j)-th DWT coefficient. In the following, we define local energy index of a coefficient block as follows.
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Definition 1 Local energy index (LEI). The local energy index of a coefficient block Cuv is defined by 1/2
⎛ m1 m2 ⎞ euv = LEI uv = ⎜ ∑∑ cuv2 (i, j ) ⎟ ⎝ i =1 j =1 ⎠ .
(2)
Fig.1 The Wavelet Decomposition with One Level. (a) Original Image I with Size M1× M2. (b) Wavelet Decomposition with One Level for Image I. (c) Four Sub-bands of the Image I, where LL1 is Low-frequency (or Approximation) Sub-band.
Fig.2 Block-wise Operation for Low-frequency Sub-band LL1. (a) The Low-frequency Sub-band LL1 of an Image (with size [M1/2]× [M2/2]). (b) All DWT Coefficient Blocks with size 4×4. (c) The 16 DWT Coefficients Contained in a Coefficient Block.
In this paper, local energy index (LEI) is regarded as the object of quantization modulation. Suppose Δ is a quantization step. The modulation rule of local energy index of a coefficient block is as follows.
⎧[e Δ ] ⋅ Δ + 3 ⋅ Δ / 4, if e 'uv = ⎨ uv ⎩ [euv Δ ] ⋅ Δ + Δ / 4, if
wuv = 1 wuv = 0
(3)
where e’uv expresses local energy index after modulating, wuv is the watermark bit to be embedded, 1≤u≤[M1/2], 1≤v≤[M2/2]. From above modulation rule, we can see that each coefficient block hides only a watermark bit. In order to realize above modulation of local energy index, we need modulate every DWT coefficient of a coefficient block. For this purpose, we will accomplish the modulation of local energy index by modifying every DWT coefficient of the coefficient block. In the following, we illustrate our coefficient modulation strategy as follows. Suppose ∇ is desired local energy index after modulating. For a coefficient block Cuv, if euv
