International Journal of Electronics, Communication & Instrumentation Engineering Research and Development (IJECIERD) ISSN 2249-684X Vol. 3, Issue 1, Mar 2013, 97-104 © TJPRC Pvt. Ltd.

WATERMARKED AND TRANSMISSION THROUGH DIGITAL COMMUNICATION SYSTEM

M. VENU GOPALA RAO 1, N. NAGA SWETHA 2, B. KARTHIK 3, D. JAGADEESH PRASAD 4 & K. ABHILASH 5 1Professor, Department of ECE, K L University, Vaddeswaram, Andhra Pradesh, India 2,3,4,5 Student, Department of ECE, K L University, Vaddeswaram, Andhra Pradesh, India

ABSTRACT

This paper presents a process able to mark digital images with an invisible and undetectable secrete information, called the watermark, followed by compression of the watermarked image and transmitting through a digital communication system. This process can be the basis of a complete copyright protection system. Digital water marking is a feasible method for the protection of ownership rights of such as audio, image, and other data types. The application includes digital signatures, fingerprinting, broadcast and publication monitoring, copy control, authentication, and secret communication. For the efficient transmission of an image across a channel, source coding in the form of image compression at the side & the image recovery at the receiver side are the integral process involved in any digital communication system. Other processes like channel encoding, signal at the transmitter side & their corresponding inverse processes at the receiver side along with the channel equalization help greatly in minimizing the bit error rate due to the effect of noise & limitations (or the channel capacity) of the channel. The results shows that the effectiveness of the proposed system for retrieving the secret data without any distortion.

KEYWORDS: Channel Equalization, Digital Communication System, Image Compression, Water Marking

INTRODUCTION

Digital media are subjected to illicit distribution of data and owners of data are cautious about making their work available without some method of identifying ownership and copyright. Digital watermarks are employed in an attempt to provide proof of ownership and identify illicit copying and distribution of multimedia information[4]. The growth of digital media and the fact that unlimited numbers of perfect copies of such media can be illegally produced is a threat to the rights of content owners. A copy of digital media is an exact duplicate of the original. The authors of a work are hesitant to make such information available on the as it may be copied and retransmitted without the permission of the author. An issue facing electronic commerce on the Internet for digital information is how to protect the copyright and intellectual property rights of those who legally own or posses digital works.

Most electronic commerce systems use cryptography to secure the electronic transaction process. Encryption provides data confidentiality, authentication, data integrity, and in some cases authentication of the parties involved. However, the unencrypted data may still be copied and distributed (i.e., videotapes, DVD, and pay-per-view broadcasts). One approach to copyrighting is to mark works by adding information about their relationship to the owner by a digital watermark[4]. Digital watermarking provides a means of placing information within digital works. This information may be perceptible or imperceptible to the human senses. Watermarking can be used to identify owners, license information or other information related to the cover carrying the watermark. Watermarks may also provide some control mechanisms such as determining if the work has been tampered with or copied illegally. When watermarked images are found, the 98 M. Venu Gopala Rao, N. Naga Swetha, B. Karthik, D. Jagadeesh Prasad & K. Abhilash information is reported back to the registered owners of the images[4]. This paper demonstrates how secret information is transmitted and received through a digital communication system. The secret information is embedded in a using Discrete transform.

For the efficient transmission of an image across a channel, source coding in the form of image compression at the transmitter side & the image recovery at the receiver side are the integral process involved in any digital communication system. DCT based compression technique is used for compression purpose[1]. Other processes like channel encoding, signal modulation at the transmitter side & their corresponding inverse processes at the receiver side along with the channel equalization help greatly in minimizing the bit error rate due to the effect of noise & bandwidth limitations (or the channel capacity) of the channel[6]. Binary Phase Shift Keying (BPSK) modulation and techniques used for digital transmission purpose. The results shows that the effectiveness of the proposed system for retrieving the secret data without any distortion.

This paper organized as follows. Chapter-II describes briefly about digital watermarking. Image compression using DCT is illustrated in Chapter-III. The BPSK digital communication system is described in Chapter-IV. The simulation results are discussed in Chapter-V and the references are given at the end.

DIGITAL WATERMARKING

Digital watermark means embedding information into digital material in such a way that is imperceptible to a human observer but easily detected by computer algorithm[4]. A digital water mark is a transparent, invisible information pattern that is inserted into a suitable component of the data source by using a specific computer algorithm. Digital watermarks are signals added to digital data (audio or video or still images) that can be detected or extracted later to make an assertion about the data.

All watermarking techniques share the same generic build blocks: a watermark embedding system and a watermark decoder system[4]. As for the above example, the secret images S embeds into the original image L through the encoder and watermarked image L’ is created. The secret image can be extracted from the watermarked image through the decoder. Different watermarking methods can be adopted in the encoder and decoder. The method illustrated here is using Discrete Wavelet Transform (DWT)[7]. 1 Level DWT is applied to original image which transforms it into four subbands, Watermarked Image Compression and Transmission through Digital Communication System 99 i.e., LL , LH , LH and HH components, where ‘ L’ is low frequency and ‘ H ’ is high frequency. The secret data is embedded into any one of the high frequency subband components. Digital watermarks can be measured on the basis of certain characteristics and properties that depend on the type of application. In general, they are described as fidelity, robustness, fragility, tamper resistance, data payload, complexity, and other restrictions].

IMAGE COMPRESSION

The discrete cosine transform (DCT) is a technique for converting a signal into elementary frequency components[2]. It is widely used in image compression.

Digital images require huge amounts of space for storage and large bandwidths for transmission. Image compression is used to reduce or eliminate redundant or irrelevant information[3]. The two main Image compression techniques are and . Here we use lossy compression using DCT[2].

Discrete Cosine Transform & Quantization Followed by Zigzag Traversing

The JPEG lossy compression algorithm does the following operations[3]:

• First the lowest weights are trimmed by setting them to zero.

• The remaining weights are quantized (that is, rounded off to some nearest of discrete code represented values), some more coarsely than others according to observed levels of sensitivity of viewers to these degradations. The accuracy of quantization depends on the number of quantization levels taken.

Figure 3: Typical Discrete Transform of 4x4 Image Block

Figure 4: Quantization Followed by Zigzag Traversing 100 M. Venu Gopala Rao, N. Naga Swetha, B. Karthik, D. Jagadeesh Prasad & K. Abhilash

DIGITAL COMMUNICATION SYSTEM

Figure 5: Block Diagram of the Digital Communication System

The three basic elements of every communication systems are Transmitter, Receiver and Channel [6]. The Overall purpose of this system is to transfer information from one point (called Source) to another point, the user destination. The transmitter is located at one point in space, the receiver is located at some other point separate from the transmitter, and the channel is the medium that provides the electrical connection between them.

The purpose of the transmitter is to transform the message signal produced by the source of information into a form suitable for transmission over the channel. The received signal is normally corrupted version of the transmitted signal, which is due to channel imperfections, noise and interference from other sources. The receiver has the task of operating on the received signal so as to reconstruct a recognizable form of the original message signal and to deliver it to the user destination[3].

SIMULATION RESULTS AND DISCUSSIONS

Different output parameters like SNR & Compression Ratio determine the efficiency of system. These parameters in turn depend mainly on different input parameters like number of quantization levels, number of diagonals considered for Zigzag traversing (or simply, the percentage of pixels neglected), size of blocks taken from image for DCT transform & in some cases various other parameters like Signal to Noise Ratio (snr) in the transmission channel.

Resulting images of different sized gray images by varying the Block size taken for DCT & number of coefficients selected for transmission and Quantization Level is fixed for the entire observation.

Watermarked Image Compression and Transmission through Digital Communication System 101

The values of corresponding input & output parameters for different sized gray images by varying the Block size taken for DCT & number of coefficients selected for transmission are tabularized as shown. Quantization Level is fixed for the entire observation.

Table 1: Images with Varying Block Sizes and No. of Coefficients and Fixed Quantization Levels

Resulting images of different sized gray images by varying the Quantization Level & Block size taken for DCT are tabularized as shown. Number of Coefficients selected for transmission is kept fixed for the entire observation.

The values of corresponding input & output parameters for different sized gray images by varying the Quantization Level & Block size taken for DCT are tabularized as shown. Number of Coefficients selected for transmission is kept fixed for the entire observation. 102 M. Venu Gopala Rao, N. Naga Swetha, B. Karthik, D. Jagadeesh Prasad & K. Abhilash

Table 2: Images with Varying Quantization Levels and Fixed Block Sizes and Number of Coefficients

Thus we see that the SNR (of received image) & the compression ratio are directly affected by changes in quantization level & number of diagonals. As expected the SNR increases & compression ratio decreases by increasing the number of diagonals & number of quantization levels, though the effect of quantization level is more pronounced.

Apart from such obvious results, it can also be noticed that SNR decreases & compression ratio increases with the increase in the block size taken for DCT (keeping the percentage of pixels taken to be almost constant with respect to block sizes). This behaviour can be explained on the fact that a longer string of continuous zeros can be obtained (after neglecting the similar percentage of pixels) by increasing the block size.

One more behaviour worth analyzing is that when the block size taken for DCT is increased to 16X16, then on increasing the participating number of diagonals compression ratio is decreased as expected but the SNR also reduces (though very slightly).

This again can be explained on the basis of the fact that an increasing number of symbols are being quantized by the same number of quantization level resulting an increase in quantization error. So, in this case SNR can be increased by increasing the number of quantization levels.

Where as in case of compression using Discrete Wavelet Transform, it can be observed that for a fixed Decomposition Level, the increase in value of Threshold results in greater compression. While for a fixed value of Threshold, compression score/ratio decreases with increase in Decomposition Level. Also better compression results are obtained for images of larger size.

Scope of Improvement

The system designed above faces the shortcoming of sometimes not recovering the image at all if any of the received bit is erroneous with respect to the bits transmitted after redundancy reduction though DCT or DWT followed by Huffman Coding, i.e. the distortion due to transmission channel have to be reduced properly by channel equalization & bit errors, if present, should be accurately detected & corrected by Channel Decoding.

Thus system can be modified & designed in such a way that it is able to decode the part of compressed transmitted data which is errors free, thereby identifying & either correcting or leaving the erroneous data. Watermarked Image Compression and Transmission through Digital Communication System 103

REFERENCES

1. Andrew B. Watson, “Image Compression Using the Discrete Cosine Transform”, Mathematica Journal, 4(1), 1994, p. 81-88.

2. Giridhar Mandyam, and Neeraj Magotra, “Lossless Image Compression Using the Discrete Cosine Transform” Journal of visual communication and image representation, vol. 8, no. 1, march, pp. 21–26, 1997.

3. Rafael C Gonzalez, Richard E Woods,” ”, Second Edition, Pearson Education Asia, 2002.

4. Bors, A.G. ,Dept. of Inf., Thessaloniki Univ.,Pitas, “Image watermarking using DCT domain constraints” Volume: 3 Page(s): 231 - 234 vol.3.

5. Paul G. Howard and Jeffrey Scott Vitter, “Fast and Efficient Lossless Image Compression”, proceedings of IEEE Computer Society/NASA/CESDIS Conference, Snowbird, Utah, 1993, pages 351-360.

6. Proakis, “Digital Communication” , Third edition,jgjgc,kjgwfkhfkh

7. “Analysis of discrete Wavelet based image compression” Journal Of Scientific and industrial research,Volume 68.