Comparison Of Wavelets To Watermarking Applications

Comparison Of Wavelets To Watermarking Applications

Umaamaheshvari. A, K.Thanushkodi

1Assistant Professor/ECE/SSEC/Coimbatore/Tamilnadu 641 104

2Director/ACET

Abstract

Healthcare infrastructure depends on Hospital Information Systems (HIS), Radiology Information Systems (RIS),Picture archiving and Communication Systems (PACS) as these provide new ways to store, access and distribute medical data . It reduces the security risk. Conversely, these developments have introduced new risks for unsuitable deployment of medical information flowing in open networks, provided the effortlessness with which digital content can be manipulated. Watermarking is a budding technology to overcome these drawbacks. In the proposed work a block based error correction code to improve the quality of watermark image. The watermark is done in all types of wavelets and a comparison is given. When the image is watermarked using different wavelets the wavelets give somewhat similar results. In the proposed method peak signal to noise ratio, structural similarity index measure and correlation are calculated. It is compared with the existing method and found to be effective.

1. Introduction

   Due to the development of technology, digital watermarking or Steganography, an invisible signal is usually embedded into a digital medium. It may be an image, audio, or video data to protect it from unauthorized use and alteration, so that the content of information and the source can be authenticated. The lossless data embedding is called as reversible data embedding. It embeds invisible data called payload into a digital medium. Such an image in a reversible fashion so that the original image and the payload is lossless recovered. The most important requirement of lossless watermarking is that any distortion between the original image and the watermarked image should be perceptually invisible. The human visual system (HVS)[1] is the perceptual phenomenon that can be exploited to achieve this requirement. The data hiding

   technologies for digital data like digital watermarking have attracted enormous attention recently [2].

   A watermark is put into the media and cannot be removed or altered easily. The watermarking process introduces irreversible degradation of the original medium. Even though the degradation is less, it may not be acceptable to certain applications, like military and medical use .So there is a need for a reversible watermark which completely recovers the original image. If the embedding algorithm and embedding parameters are available, it is possible to detect the watermark from the marked medium to recover the original medium. But most watermarking algorithms apply some non-linearity to optimize the performance of the algorithm. Therefore, a reversible watermark must be designed such that it can be removed to restore the original medium without any reference to information beyond what is available in the watermarked medium .

   The proposed scheme divides an input image into non overlapping blocks of a given size and Integer wavelet transform is taken for each small block and then data is embedded into the high- frequency wavelet coefficients of each block. Various block sizes and their performance are studied in it. A lossless data hiding method using integer wavelet transform is given by [3]. In that small coefficients of the high frequency sub band are modified to embed data. The histogram modification is done to prepare enough space for data hiding . Weng [4] proposes the reversible integer transform using the correlations among four pixels in a quad. Data embedding is done by expanding the differences between one pixel and each of its three neighbouring pixels. To improve the hiding capacity, difference expansion and companding technique is used in the embedding process. [5] Kim Proposes a difference expansion method with simplified location map and better embedding capacity can be achieved with new expandability .

   n

   n

2. Types of Wavelets

   n 1

   f x

   cos

   j k 1

   (1)

   2.1Discrete wavelet transform

   Wavelets can be described as functions

   j k

   k 0

   2

   defined over a finite interval and having an average value of zero. The fundamental idea of the wavelet transform is to denote any arbitrary function as a superposition of a set of such wavelets or basis functions [6]. These wavelets are acquired from a single mother wavelet through multiplicative scaling and translational shifts. The large number of known wavelet families and functions provides a rich space in a variety of applications. Biorthogonal, Coiflet, Haar, Symmlet, Daubechies wavelets [7] and the like, are some of the wavelet families.

   1. Coiflet Wavelets

      Coiflets are designed by Ingrid Daubechie. The wavelet is symmetric having scaling function and wavelet function. Is it considered the scaling function as low pass filter and wavelet function as high pass filter after normalization. Each scaling and wavelet function has particular coefficient value.

   2. Daubauchie wavelets

      The daubechie wavelets are of orthogonal wavelets. It defines a discrete wavelet transform and characterized by a maximal number of vanishing moments. The father wavelet generates the orthogonal multi resolution analysis. The vanishing moment limits the wavelets ability to represent polynomial behaviour or information in a signal. All types of Daubechie wavelets are analyzed.

   3. Haar Wavelet

   Harr wavelet is a sequence of square- shaped wavelet function. It is represented in terms of an orthonormal basis function. It is similar to Fourier analysis. It is also known as D2 wavelet.

3. Discrete Cosine Transform

4. Performance metrics

   1. Mean Square Error

      Mean Square Error (MSE) for two P×Q monochrome images (G and R) where one of the images is considered a noisy approximation of the other is defined as:

      (2)

   2. Peak Signal to Noise Ratio

      The PSNR is most commonly used as a measure of the quality of despeckled image. The PSNR is defined as:

      (3)

      where is the maximum intensity in the unfiltered image. A higher PSNR would normally indicate that the reconstruction is of higher quality.

   3. Structural Similarity Index Measure

      This SSIM is defined as:

      (4)

   4. Normalized Correlation

      For image-processing applications in which the brightness of the image and template can vary due to lighting and exposure conditions, the images can be first normalized. This is typically done at every step by subtracting the mean and dividing by the standard deviation.

      The Discrete Cosine Transform is a renowned coding technique employed in image and video compression algorithms. It is capable of carrying out decorrelation of the input signal in a data-independent manner [8]. The DCT is a methodology for the transformation of a signal into elementary frequency components. The sequences

5. Experimental Results and Comparison

   (5)

   of n real numbers x1 ,, xn

   are converted into the

   The experimental results of the proposed

   effective watermarking scheme for checking the

   sequence of n complex numbers

   f1,, fn

   by the

   integrity and authenticating medical images using hybrid transform (DCT-DWT). The proposed

   DCT [9] in accordance with the following formula:

   watermarking scheme is programmed in Matlab (Matlab7.9) and tested with different types of

   avelets. The proposed watermarking scheme discussed in this paper effectively embedded the watermark image into the original image and extracted it back from the watermarked image. The watermarked images possess superior Peak Signal to Noise Ratio (PSNR), Structural similarity Index Measure(SSIM) and visual quality. The watermark and watermarked images of different original CT (Computed tomography) medical images are shown in Table 1,2,3,4 and 5 along with the PSNR values

   Table 1: For CT Images

   Table 2 :FOR MRI IMAGE

   Table 3:FOR ULTRA IMAGE

   Table 4: FOR LENA IMAGE

   Table 5: COMPARISON WITH EXISTING METHOD

6. Conclusion

   On comparing the medical images and lena image using MATLAM 7.9 version it is observed that the PSNR is almost same in all the wavelet families except with the fractional part. With this result we can go for the optimization of watermarking method which will be helpful for the future research on the enhancement of the software.

7. References

1. Fori P., and Levický D., Implementations Of HVS Models In Digital Image Watermarking, Journal of Radio engineering, Vol. 16, No. 1, pp. 45-50, April 2007.

2. Prabhishek Singh, and R S Chadha .,A Survey of Digital Watermarking Techniques, Applications and Attacks , International Journal of Engineering and Innovative Technology,Vol.2 Issue 9,2013

3. Yousefi, S. Rabiee, H.R.Yousefi, E.Ghanbari, M. Sharif, Reversible Date Hiding Using Histogram Sorting and Integer Wavelet Transform Univ. of Technol., Tehran Digital Eco Inaugural ,IEEE-IES Volume , Issue , 21-23 Feb. 2007 Page(s):487 490 [4]Shaowei Weng; Yao Zhao; Jeng-Shyang Pan;Rongrong Ni ,A Novel Reversible Watermarking Based on an Integer Transform Image Processing, 2007. ICIP 2007. IEEE International Conference on Volume 3, Issue ,Sept. 16 2007-Oct.19 2007 Page(s):III – 241 – III 244.

1. Hyoung Joong Kim, Member, IEEE, Vasiliy Sachnev, Yun Qing Shi, Fellow, IEEE, Jeho Nam, SeniorMember, IEEE, and Hyon-Gon Choo A Novel Difference Expansion Transform for Reversible Data

   Embedding IEEE Transactions on Information Forensics and Security, vol. 3, no. 3, September 2008 pages456-465.

2. L. Kamstra and H. J. A. M. Heijmans, Reversible data embedding into images using wavelet techniques and sorting,IEEE Trans. Image Process., vol. 14, no. 12, pp. 20822090, Dec. 2005.

3. Daubechies, I., Ten Lectures on Wavelets, SIAM, Philadelphia, 1992.

4. Christopher Bennett, "DFT, DCT, and DWT",

   University of Miami, 2005

5. Ms.Rekha D.Patil1, Mr.A.R.Nigavekar, Reversible Image Watermarking Using Lifting Wavelet Transform And Arithmetic Coding, International Journal of Engineering Research & Technology vol 2 issue 2 2013.