ROBUST AND OBLIVIOUS IMAGE WATERMARKING SCHEME IN THE DWT DOMAIN USING GENETIC ALGORITHM K. Ramanjaneyulu 1, K. Rajarajeswari 2

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1 Research Article ROBUST AND OBLIVIOUS IMAGE WATERMARKING SCHEME IN THE DWT DOMAIN USING GENETIC ALGORITHM K. Ramanjaneyulu, K. Rajarajeswari 2 Address for Correspondence Department of Electronics and Communications Engineering, Bapatla Engineering College, Acharya Nagarjuna University, Andhra Pradesh, India 2 Department of Electronics and Communications Engineering, College of Engineering, Andhra University, Andhra Pradesh, India ABSTRACT In this work, a robust and oblivious image watermarking scheme based on Discrete Wavelet Transform (DWT) for copyright protection is presented. The original unmarked image is not required for watermark extraction. Second level DWT is applied to the original cover image and horizontal detail sub band (LH 2 ) is selected for embedding a binary watermark. Coefficients of the LH 2 sub band are divided into non-overlapping blocks. Number of blocks must be equal to the number of watermark bits. In each block, the first minimum and the second minimum are identified and modified according to the watermark bit. After watermark insertion, inverse DWT is applied to the sub bands with modified coefficients to obtain the watermarked image. For watermark extraction, a threshold based decoder is designed. Embedding and extraction process is characterized with parameters and Genetic Algorithm (GA) is used for parameter optimization. Optimization is to maximize the values of Peak Signal to Noise Ratio (PSNR) of the watermarked image and Normalized Cross correlation (NCC) of the extracted watermark. Improved embedding capacity and the use of parameter optimization for better performance are the advantages of the proposed method in comparison with the existing method. Experimental results show that, this algorithm is highly robust for many image attacks on the watermarked image. KEY WORDS: Digital watermarking, Discrete Wavelet Transform, Genetic Algorithm, NCC, PSNR. INTRODUCTION Major problem associated with digital multimedia data transfer over internet is copyright protection. This is because; digital media are easy to copy and transmit. Many researchers are also aware of the issues like copyright protection, authentication, proof of ownership, etc., related to multimedia. Many solutions have been proposed to solve those problems and are available in the literature. Watermarking technique is one of the popular solutions to those issues. Watermarking technique embeds specific information called watermark into the original media so that it is not easily perceptible; that is, the viewer cannot see any information embedded in the contents. In the case of dispute over the owner of the multimedia data, embedded watermark can be extracted and it can be used to identify the owner because the watermark contains the required information. There are several important issues in the watermarking system. First, the embedded watermark should not degrade the quality of the image and should be perceptually invisible to maintain its protective secrecy. Second, the watermark must be robust enough to resist common image processing attacks and not be easily removable; only the owner of the image ought to be able to extract the watermark. Third, the blindness is necessary if it is difficult to obtain the original image and watermark at time of extraction. A watermarking technique is referred to as blind (also referred to as oblivious or public) if the original image and watermark are not needed during extraction. The process of digital watermarking involves the modification of the original multimedia data to embed a watermark containing key information such as authentication or copyright codes. The embedding method must leave the original data perceptually unchanged, yet should impose modifications which can be detected by using an appropriate extraction algorithm. Common types of signals to watermark are images, music clips and digital video. Application of the digital watermarking to still images is considered in this work. If the watermark survives for many image attacks and is still extractable from the watermarked media then the watermarking technique is called robust. The major technical challenge is to design a highly robust digital watermarking technique which protects copyright of the media owner by making the process of watermark removal tedious and costly []. Watermarking schemes can be classified into several categories according to their applications, embedding domain and characteristics. Based on embedding domain, watermarking schemes are classified into three categories: spatial domain, transform domain and hybrid domain. Algorithms based on the transform domain are more robust than the schemes based on spatial domain and hybrid domain. Watermarking algorithms based on Discrete Cosine Transform (DCT) [2], [3], [4], [5], Discrete Wavelet Transform (DWT) [6], [7], [8], [9], Discrete Hadamard Transform (DHT) [0], [], Singular Value Decomposition (SVD) [2], and Discrete Fourier Transform (DFT) [3], [4] are some of the transform

2 domain methods available in the literature. Image watermarking is the process of inserting an image called watermark in another image called cover image. Outcome of this insertion process is called watermarked image. The insertion process must be in such way that watermark is extractable from the watermarked image. The main problem in digital image watermarking is survival of the watermark when the watermarked image is subjected to image processing operations (image attacks) like compression, low-pass filtering, high-pass filtering, histogram equalization etc. Watermarking schemes are called robust if the survival capability of the watermark is high. If the extraction process does not requires watermark or cover image then the scheme is called oblivious watermarking. Several algorithms are available in the literature for robust and oblivious image watermarking. But, there are some limitations in the existing algorithms. Designing a robust and oblivious watermarking system is still a challenging problem. Watermark embedding in the cover image requires modification of some features of the cover image according to the symbols of the watermark. Selected features of the cover image can be pixels or transform coefficients. If the watermarking algorithm modifies the cover image pixels then it is called spatial domain watermarking. Transform domain watermarking algorithms modifies the transform coefficients of the cover image. Spatial domain algorithms are simple and data embedding capacity is more. But they are not robust against image attacks. On the other hand, transform domain algorithms are robust against image attacks and the amount of robustness varies from attack to attack. No single algorithm is available with which watermark survives for all possible image attacks. Hence, there is a scope for improvement in the excising watermark algorithms and an attempt is made to improve the performance of some of the existing transform based robust and oblivious watermarking algorithms. Lin et al., [8] proposed an algorithm based on Discrete Wavelet Transform (DWT). Binary watermark of size 32 6 is embedded in the 8-bit gray scale cover image of size Cover image is decomposed using fourth level DWT. LH 4 sub band is selected for watermark embedding. Selected sub band is divided into variable size blocks. Number of blocks required is equal to the number of watermark bits. In each block of LH 4, one bit is embedded by modulating the maximum wavelet coefficient according to the watermark bit. Value of the maximum coefficient is increased for embedding a. To embed a 0, maximum coefficient value is decreased. But, the modified value must be greater than or equal to the second maximum in that block. This requirement is essential for watermark extraction. Some parameters are used to get control over the embedding and extraction process. Limitations of this method are low data embedding capacity and the difficulty of adjusting the parameters when there is a change in watermarking requirements or in input images. Another DWT based algorithm was proposed by Lin et al., [5] in which there is more scope for increasing the information carrying capacity of the cover image. Binary watermark of size 32 6 is embedded in the 8-bit gray scale cover image of size LH 4 and LH 3 sub bands are selected for watermarking. Fixed length blocks are formed with the coefficients of the selected sub bands. There is a constraint in the formation of the blocks. In each block, one coefficient from LH 4 and four coefficients from LH 3 must be included. One watermark bit can be embedded in each block by modulating the first minimum and second minimum in that block according to the watermark bit. With this frame work, maximum of 024 bits can be embedded. If the input images (cover image, watermark) are changed then the parameters of the scheme need to be adjusted for satisfying the PSNR requirement. In this paper, a GA based robust and oblivious watermarking scheme with increased data embedding capacity is presented in the DWT domain. Second level DWT is applied to the original cover image and horizontal detail sub band (LH 2 ) is selected for watermark embedding. GA is used for optimizing the parameters of the scheme. LH 2 sub band coefficients are divided into non-overlapping blocks. Number of blocks must be equal to the number of watermark bits. One watermark bit is embedded in each block. A distance vector is computed using the coefficients of these blocks. Each element in the distance vector represents the difference between the second minimum and the first minimum in a block. To embed a watermark bit 0, first minimum and second minimum in a block are assigned the same value and that value lies between first minimum and second minimum. Exact value will be selected using Genetic Algorithm (GA) for the specified watermarking requirements. To embed a watermark bit, first minimum value is decreased by a value which is the maximum of the mean value of the distance vector and a constant. GA is used to choose the constant value. After embedding all the watermark bits, inverse DWT is applied to the sub bands including modified sub band (LH 2 ) to obtain the watermarked image. For watermark extraction, a threshold based statistical decoder is designed. In that, a distance vector is

3 calculated from the possibly attacked watermarked image and by comparing each element of the distance vector with the threshold, a decision is taken in favor of one of the binary symbol. The scheme is characterized with parameters and Genetic Algorithm (GA) is used for parameter optimization. Optimization is required to satisfy the conflicting requirements of Peak Signal to Noise Ratio (PSNR) and Normalized Cross correlation (NCC). Experimental results show that, this algorithm is highly robust for many image attacks on the watermarked image. Proposed method is compared with the existing method [5] in which 32 6 size binary watermark (52 bits) is embedded in the DWT domain of the host image. Proposed method is the improved version of it and embeds size binary watermark (2048 bits) without compromising the perceptual quality (PSNR) of the watermarked image. Remainder of the paper is organized as follows: In section 2, introduction to Genetic Algorithm and its application to watermarking are described. Proposed scheme is presented in Section 3. Experimental results are given in Section 4. Conclusions are presented in Section Genetic algorithm Genetic Algorithms [GA] were first developed by John Holland [6]. GA is one of the best optimization tools available in the literature. It is widely used to solve the problems in various scientific and engineering applications [7]. GA process can be described based on five functional units. They are a random number generator, fitness evaluation unit and genetic operators for reproduction, crossover and mutation operations. Random number generator generates a set of number strings called population. Each string represents a solution to the optimization problem. For each string, a fitness value is computed by the evaluation unit. A fitness value is a measure of the goodness of the solution.the objective of the genetic operators is to transform the set of strings into sets with higher fitness values. The reproduction operator performs a natural selection function known as seeded selection. Individual strings are copied from one set (generation of solutions) to the next according to their fitness values. The probability of a string being selected for the next generation increases with the fitness value. The crossover operator chooses pairs of strings at random and produces new pairs. The mutation operator randomly changes the values of bits in a string. A phase of the algorithm consists of applying the evaluation, reproduction, crossover and mutation operations. A new generation of solutions is produced with each phase of the algorithm. Completion of optimization process depends on termination criterion. Termination criterion can be specified in terms of number of generations, specified time interval, etc. Watermarking problem can be viewed as an optimization problem. In this work, GA is used for solving the optimization problem. PSNR & NCC are the two important characteristic parameters of a watermarking system. The amount of distortion introduced to the host image during embedding process is inversely proportional to PSNR. NCC indicates the amount of similarity between original watermark and extracted watermark. Hence, both PSNR and NCC values must be as large as possible for a good watermarking system. But PSNR and NCC are related in such way that maximization of PSNR decreases the value of NCC. Hence, the watermarking scheme is characterized with parameters and GA is used to find the optimum values of parameters to obtain the specified performance of the watermarking system in terms of PSNR and NCC. 3. Proposed scheme Proposed scheme is described in three sub sections. Sub section-3. deals with watermark embedding procedure, watermark extraction is explained in sub section-3.2 and the application of GA for determining the optimum parameters of the scheme is given in sub section Watermark embedding technique This sub section describes the embedding procedure to embed a binary watermark into the 8-bit grayscale cover image. Consider the cover image of size and transform it using the 2-level DWT. This transformation produces seven sub bands. One or more sub bands can be used for watermark embedding. Depending upon the specific watermarking requirements, sub bands will be selected. The requirements considered in this work are imperceptibility, robustness and data embedding capacity. For satisfying those requirements and based on the reasons in [8], [5] and [8], LH 2 is chosen for embedding size watermark image. Coefficients of LH 2 are grouped into non-overlapping blocks. In each block, one bit of information is embedded by modifying the two coefficients in that block. The two coefficients in a block are the first minimum and the second minimum. Steps of the embedding process are as follows:. Decompose the original cover image using the 2- level DWT. 2. Organize the coefficients of LH 2 into various blocks. Number of blocks must be exactly to the number of watermark bits. Four non-overlapping coefficients from LH 2 are included in each block to obtain 4096 blocks.

4 3. Let and are the first minimum and the second minimum respectively in the block. Let denotes the difference between and. Compute for all. 4. Compute, the mean value of, where. 5. Consider a binary watermark of size and reshape it with a size of Let represents the watermark bit. To embed a bit 0 ( ), and are modified as follows: = () (2) To embed a bit ( ), is modified as follows:, if, otherwise (3) are parameters used to change the perceptible quality (PSNR) of the Watermarked image. 6. After embedding all the watermark bits, combine the blocks and form the modified LH 2 sub band. Then with this modified sub band and with the remaining unmodified sub bands; apply inverse DWT to get the watermarked image Watermark extraction process Possibly attacked watermarked image and one parameter (k4) are the inputs required for the extraction process. The parameter, k4 can be considered as the required key in the extraction process. Extraction of watermark is as follows:. Decompose the possibly attacked watermarked image into sub bands using the 2-level DWT. 2. Organize the coefficients of LH 2 into various blocks in a similar way that is used in the embedding process. 3. Let and are the first minimum and the second minimum respectively in the block. Let denotes the difference between and. Compute for all. 4. Compute, mean and standard deviation values of the vector, where., 5. Compute the following: (4) (5) (6) Where, T is the threshold and k4 is the parameter used to control the NCC value. 6. Extract the watermark using the following rule: (7) After extracting all the watermark bits, reshape them as size binary watermark Optimization of parameters using GA Optimization of parameters using GA requires initial values for the parameters of the embedding process and the fitness function. Fitness function is a user defined function and it must be a function of PSNR, NCC and the other parameters of the scheme. Optimization process starts with initialization. Hence, initial values for the parameters must be supplied before running the GA algorithm. Supplied initial values must be within the solution space and they need not represent the correct solution. GA will maximize the value of the fitness function and this maximization process will terminate according to the specified termination criteria. After the termination of GA, it gives the optimum values of parameters. Those parameters must be used in the embedding and/or extraction process. The error metrics used to test the proposed algorithm are Normalized Cross correlation (NCC) and peak signal to noise ratio (PSNR) and are defined in section 5. Optimization of parameters is as follows:. Set the initial ranges for all the parameters (four parameters, k to k4) 2. Embed a binary watermark in the gray level host image following the steps in sub section 3. and using the initial parameters. Calculate the PSNR value of the obtained watermarked image. 3. Extract the watermark from the attacked watermarked images for the specified number; say p, of attacks as per the procedure explained in sub section 3. 2 and using the initial parameters. Calculate the NCC values for the extracted watermarks.

5 4. Use GA to maximize the following fitness function and get the parameters for the optimum performance of the proposed scheme. p fitl = PSNRl + ( NCCk, l α k) (8) P k= Where, l denotes GA generation number, p denotes the total number of attacks used in the optimization process, NCC, represents NCC value with attack k l k and α k represents weighting factor for NCC. Note: Refer Fig. for the flow chart of parameter optimization process. Fitness function fitl of equation (8) is the most generalized one and it can be changed according to the watermarking requirements for some intended application. Fitness functions used in this work are given in the experimental results section. Host image h Yes Final Iteration GA selection Fitness No Optimized Watermarked Image Embedding with GA parameters htemp PSNR calculation Fig. Flow chart for GA based watermark embedding 4. Experimental results The peak signal-to-noise ratio (PSNR) is used to evaluate the quality of the watermarked image in comparison with the host image. PSNR Formula is as follows: (9) PSNR= 0log0 db M N 2 [ f ( i, g( i, ] M N x= y= Fitness function Watermarked image, h Various attacks Watermark Extraction h Temp Where, M and N are the height and width of the image, respectively. f(i, and g(i, are the pixel values located at coordinates (i, of the original image, and the attacked image, respectively. After extracting the watermark, the normalized correlation coefficient (NCC) is computed using the original watermark and the extracted watermark to judge the existence of the watermark and to measure W NCC Calculation Watermak image W the correctness of an extracted watermark. It is defined as m n [ w( i, wmean][ w ( i, wmean ] (0) i= j= NCC = m n m n 2 2 [ w( i, wmean ] [ w ( i, wmean ] i= j= i= j= Where, m and n are the height and width of the watermark, respectively. The symbols w( i, and w o ( i, are the bits located at the coordinates ( i, of the original watermark and the extracted watermark respectively. The symbols w mean and w mean are the mean values of the original watermark and the extracted watermark respectively. Lena image of size is used as the cover image and is shown in Fig. 2. The watermark image is of size, which is a binary logo as shown in Fig. 3. Watermarked Lena image is shown in Fig. 4. Extracted watermark is shown in Fig. 5. Two dimensional DWT with Haar wavelet filters is used. Parameters of the scheme are k, k2, k3 and k4. Perceptibility of the watermarked image is controlled by k, k2, and k3. Parameter k4 controls the quality of the extracted watermark. GA is used to find the optimum values of these parameters for some target values for PSNR, NCC, and one or more specific image attacks. For testing the robustness of the proposed scheme MATLAB 7.0 and Checkmark.2 [9] are used. For all the attacks of Checkmark.2 and MATLAB 7.0, a window size of 3x3 is taken. Various attacks used to test the robustness of the proposed watermark are JPEG Compression, Median Filter, Gaussian filter, Average filter, Image Sharpening, Histogram Equalization, Resizing, Cropping, Gaussian noise, Salt & Pepper Noise, Rotation, Wiener Filter, Grey Scale Inversion, Gamma Correction, Soft Thresholding, Template Removal, Trimmed Mean Alpha, Bit plane Removal, Row & Column Copying and Row & column Blanking. Fig. 2 Cover image Lena Fig. 3 Watermark Image

6 Performance of the proposed method is compared with [5] and is shown in TABLE. Extracted watermarks, after applying various attacks are summarized in TABLE 2. TABLE 3 shows the GA results of the proposed method in fixing the parameters for the specified requirements. Fig. 4 Watermarked image Fig. 5 Extracted (PSNR=39.724dB) Watermark image TABLE 2 shows the NCC values of extracted watermarks from attacked watermarked images. Watermark image is shown in Fig. 3. PSNR value of the unattacked watermarked image (Fig. 4) is db. Parameters used in the embedding process are k, k2 and k3. The threshold parameter, k4 is used in the extraction process. The four parameter values are 0.597, 9.282,.0555, and Those values are obtained using GA with (42- PSNR) +20(-NCC) as the fitness function and with sharpening filter attack (Refer TABLE 3). TABLE 3 shows the results of GA optimization process. The attack used in the optimization is JPEG compression with a quality factor 40. Fitness Function is (42-PSNR) +20(-NCC). Range for each parameter must be given as input to the optimization process. 0. to 0.9 is the range given for k. Similarly, the ranges for k2 (5 to 25), k3 (0.5 to.5) and k4 (.0 to 6.0) are specified. Lena image is used as a cover image (Fig. 2) and the image shown in Fig. 3 is used as watermark. PSNR value of the watermarked image depends on k, k2 and k3. By changing these parameter values, PSNR value can be adjusted to any desired value. NCC value of the extracted watermark depends on k4 value. PSNR and NCC are related. NCC decreases as PSNR increases. But, the requirement is that both must be as large as possible. Hence, it is very difficult to adjust the parameter values for satisfying watermarking requirement. Using the above specified fitness function, PSNR is targeted to 40 db and NCC is targeted to.0. GA is used to minimize the value of the fitness function. As the fitness function value decreases, PSNR approaches 42 and NCC approaches.0. As explained in section 2, optimization process will terminate according to the specified termination criteria. In this work, the number of GA generations is used as the termination criteria. The first row in TABLE 3 shows the results after the first GA generation. TABLE shows the results up to five generations. Increasing GA generations can be stopped if there is no improvement in both PSNR and NCC. TABLE Comparison of proposed method With W. H. Lin et al. Method [5] Characteristic W. H. Lin et al Method [5] Proposed Method No of Watermark Bits embedded 52 (32x6 logo) 4096 (64x64 logo) Method used for determining parameters of scheme Trail and error Method GA based optimization method Type of embedding Oblivious Oblivious TABLE 2 Extracted Watermarks and NCC values Attack NCC Attack NCC No Attack.0000 Rotation JPEG Compression Rotation (QF=50%) JPEG Compression (QF=70%) Median Filtering (3x3) Gaussian filter (3x3) 0.96 Wiener Filtering Grey Scale Inversion Gamma Correction (Gamma=0.9) Average filter (3x3) Soft Threshold (3x3) Image Template Sharpening Removal Histogram Trimmed Mean Equalization Alpha Resizing (50%) Bit plane Removal Cropping (257:52,257:52 ) Gaussian noise (0.00 density) Salt & Pepper Noise (0.00 density) (LSB) Row & Column copying (0-30,40-70,00-20) Row column Blanking 30,70,20 Linear Motion (9 pixels, 0 degrees) CONCLUSIONS In this paper, a robust and oblivious watermarking scheme based on DWT is presented. Watermark is embedded in the detail sub band coefficients of the cover image. LH2 sub band coefficients of the cover image are used to form different blocks. In each block, the first minimum and the

7 second minimum are identified and one watermark bit is embedded in them. Table 3 GA Results Attack: JPEG-40 Fitness Function: (42-PSNR)+20(-NCC) Parameter value Ranges: [0., 0.9; 5, 25; 0.5,.5;.0, 6.0] Cover Image: Lena (Fig. 2) No. of Gen and iterations Fitness value PSNR In db NCC Parameters [k, k2, k3, k4] (20) [0.7872, ,.3282, 2.89] 2 (40) [0.4260, 9.679, , ] 3 (60) [0.5275, ,.063, 2.77] 4 (80) [.2229, ,.4573,.4466] 5 (00) [0.597, 9.282,.0555,.3206] 6 (20) [0.7049, ,.4609,.9453] To embed a 0, the difference between the first minimum and the second minimum is reduced to zero and to embed a, the difference is increased to a significant value. This embedding process is characterized with four parameters. GA is used to find the optimum values for the parameters. Optimization is to maximize the values of PSNR and NCC. It has been observed experimentally that the performance of the scheme is satisfactory for several image attacks. Proposed scheme is compared with the existing scheme. Compared to [5], proposed GA based method is superior in terms of the embedding capacity and survival to number of image attacks. In addition to that the flexibility of the proposed GA based scheme is demonstrated in fixing the parameters of the scheme. 6. REFERENCES. J. Zhao, Look, it s not there, Byte, January I. J. Cox, J. Killian, T. Leighton, T. Shamoon. Secure spread spectrum watermarking for multimedia, IEEE Transactions on Image Processing 6, 997, pp C. I. Podilchuk, W. Zeng, Image adaptive watermarking using visual models, IEEE Journal on Selected Areas in Communications 998; 6: Chu, W.C., 2003, DCT based image watermarking using sub sampling, IEEE Transactions Multimedia, pp Wang, Y., Alan Pearmain., Blind image data hiding based on self reference, Pattern Recognition Letters 25, pp Barni, M., Bartolini, M., Piva, F.V., 200. Improved wavelet based watermarking through pixel-wise masking, IEEE Transactions on Image Processing, 0, pp Lee, C., Lee, H., Geometric attack resistant watermarking in wavelet transform domain, Optics Express vol.3, no.4, and pp Wei-Hung Lin, Shi-Jinn Horng, Tzong-Wann Kao, Pingzhi Fan, Cheng-Ling Lee, and Yi Pan, An Efficient Watermarking Method Based on Significant Difference of Wavelet Coefficient Quantization, IEEE Transactions on Multimedia, Vol. 0, No. 5, August K. Ramanjaneyulu and K. Rajarajeswari, A New Oblivious Watermarking Method Using Maximum Wavelet Coefficient Modulation, Technology Today, Quarterly Journal, Vol. 2 No. 2, pp. 8-86, July- August K. Ramanjaneyulu and K. Rajarajeswari, An Oblivious Image Watermarking Scheme Using Multiple Description Coding and Genetic Algorithm, IJCSNS, International Journal of Computer Science and Network Security, Vol. 0 No. 5, PP , May K. Ramanjaneyulu and K. Rajarajeswari, An Oblivious and Robust Multiple Image Watermarking Scheme using Genetic Algorithm, International Journal of Multimedia and Applications (IJMA), Vol. 2 No. 3, pp. 9-38, August Yongdong, Wu, On the Security of an SVD based ownership watermarking, IEEE transactions on Multimedia, 2005, vol. 7, No P. Premaratne, A novel watermark embedding and detection scheme for images in DFT domain, Proceedings of IEE 7th International Conference on Image Processing & Applications, Vol.2, pp , V.Solachidis and V.Pitas, Circularly symmetric watermark embedding in 2-D DFT domain,ieee Transactions on Image Processing, Vol.0, pp , Wei-Hung Lin, Yuh-Rau Wang, Shi-Jinn Horng, A wavelet-tree-based watermarking method using distance vector of binary cluster, Expert Systems with Applications 36 (2009) Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. Cambridge, MA: MIT Press; Veysel Aslantas, A singular-value decompositionbased image watermarking using genetic algorithm,

8 International Journal of Electronics and Communications (AEÜ) 62 (2008) J. M. Shapiro, Embedded image coding using zerotrees of wavelet coefficients, IEEE Trans. Signal Process., vol. 4, no. 2, pp , Dec www. Author profiles K. Ramanjaneyulu is currently working as a Professor in the ECE Department, Bapatla Engineering College, Bapatla, India. He has submitted his Ph.D in AU College of Engineering, Vishakhapatnam, India. He received his M.Tech. from Pondicherry Engineering college, Pondicherry, India. He has seventeen years of experience in teaching undergraduate students and post graduate students. His research interests are in the areas of image watermarking, and wireless communications. K. Raja Rajeswari obtained her BE ME and PhD degrees from Andhra University, Visakhapatnam, India in 976, 978 and 992 respectively. Presently she is working as a professor in the Department of Electronics and Communication Engineering, Andhra University. She has published over 00 papers in various National, International Journals and conferences. She is Author of the textbook Signals and Systems published by PHI. She is co-author of the textbook Electronics Devices and Circuits published by Pearson Education. Her research interests include Radar and Sonar Signal Processing, Wireless CDMA communication technologies etc. She has guided ten PhDs and presently she is guiding twelve students for Doctoral degree. She is current chairperson of IETE, Visakhapatnam Centre. She is recipient of prestigious IETE Prof SVC Aiya Memorial National Award for the year 2009, Best Researcher Award by Andhra University for the year 2004 and Dr. Sarvepalli Radhakrishnan Best Academician Award of the year by Andhra University for the year She is expert member for various national level academic and research committees and reviewer for various national/international journals.