Synchronously Image Size Reduction using Modified Vector Quantization with Data Hiding

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 3 | Issue 02 | July 2016 ISSN (online): 2349-6010

Synchronously Image Size Reduction using Modified Vector Quantization with Data Hiding Sarita S. Kamble Department of Electronics & Telecommunication Engineering ICOER Wagholi, Pune-412207

A. S. Deshpande Department of Electronics & Telecommunication Engineering ICOER Wagholi, Pune-412207

Abstract This paper represents the scheme of data hiding and image size reduction based on side match vector quantization (SMVQ). On the sender side, residual blocks of an image compressed consecutively by SMVQ. Except for the blocks in the leftmost and topmost of the image, each of the other residual blocks is embedded with secret data. As the scheme of data compression is followed by data hiding technique. The goal of technique is to reduce the size of original image and to save storage. Compressed image is output of scheme which can be decompressed to get original image. It can be reused for various applications. Experimental results demonstrate the effectiveness of the proposed scheme in terms of compression ratio. Keywords: SMVQ, DSMVQ, Vector Quantization, Search Order Coding _______________________________________________________________________________________________________ I.

INTRODUCTION

With the fast expansion of internet technology, public be capable of transmission and sharing digital content with all easily. With the purpose of assurance of communication efficiency and save network bandwidth, compression techniques can be introduced on digital content to decrease or remove redundancy of image. Currently, the majority digital content, especially digital images as well as videos are transformed into the compressed forms for transmission. One more vital issue in an open network atmosphere is how to pass on secret or secretive data securely. Even though conventional cryptographic methods are able to encrypt the plaintext into the cipher text, the meaningless unsystematic data of the cipher text may also arouse the suspicion from the attacker. To resolve this problem, information hiding techniques have been broadly developed in educational and business. Due to the pervasiveness of digital images on the Internet, how to compress images and hide secret data into the compressed images proficiently in depth study. Among the many image compression techniques that have been proposed Vector Quantization is one of the popular. However, in all of the above mentioned schemes, data hiding is constantly conducted following image compression, which means the image compression process and the data hiding process are two self-sufficient parts on the server or sender side. Under this circumstance, the attacker may have the opportunity to capture the compressed image with no watermark information rooted. Thus, in this work, we are not only center of attention on the high hiding capacity and improvement quality, but also establish a combined data hiding in addition to compression concept with integrate the statistics hiding and the image compression into a single module effortlessly, which can avoid the threat of the attack commencing interceptors and boost the implementation efficiency. The proposed combined data hiding and image compression scheme in this paper is based on SMVQ and image in painting. On the sender side, apart from the blocks in the leftmost column and topmost row of the image, each of the other residual blocks in raster-scanning order can be embedded with secret data and compressed simultaneously by SMVQ or image in painting adaptively according to the current embedding bit. VQ is applied for some difficult residual blocks to have power over the visual distortion and fault dispersion caused by the progressive compression. After receiving the compressed codes, the receiver can segment the compressed codes into a series of sections by the marker bits. According to the index values in the segmented sections, the embedded secret bits can be extracted properly, and the decompression for every block can be achieved successfully. The remaining paper is structured as follow. In section II methodology of SMVQ and DSMVQ are described. SMVQ is data compression scheme initiates from formation of codebook. After that reducing the redundancy of codebook; we can transform image into compressed image. Side match vector quantization methodology is evaluated in detail. Experimental result is represented in section III. Conclusion and future scope of SMVQ technique are mentioned in section IV. II. PROPOSED METHOD Reduction of an image size using SMVQ Here, in SMVQ scheme, sender and receiver both have the same codebook ¥ with W codewords and all codeword of length is equal to n2. Denote the original input image (I) sized M×N. Image I is divided into n×n non-overlapping blocks. Bi, j is residual block where SMVQ is applied. Raster scan is performed on Bi, j on k divided blocks. Where,

508

Synchronously Image Size Reduction using Modified Vector Quantization with Data Hiding (IJIRST/ Volume 3 / Issue 02/ 082)

M×N n2 i=1, 2, 3…..M/n and j= 1, 2, 3…..N/n. Scrambling of secret bit is done by secret key to ensure security. k=

Fig. 1: Illustration of the prediction based on left and up neighboring pixels

Figure shows that, residual blocks are encoded progressively in raster scanning order. SMVQ is applied on it. Assume that represents a codeword, and U and L correspond to the upper and left blocks approximately it. We define the upper distortion [4] UD(y) between the codeword y and the upper block U by-

Similarly, the left distortion [4] LD(y) between the codeword y and the left block L is computed as-

The side matches distortion [4] of the codeword y is defined asSMD(y) = UD(y) + LD(y) To decode a block X, the previously encoded upper and left blocks are used to predict the sub codebook with the least side match distortion for the current block X. The generated sub codebook is then searched to find the corresponding codeword to approximate the current block. Compared with VQ, SMVQ succeeds in reducing the number of bits required to digitize the image and improves the illustration quality. As SMVQ is prediction based scheme may used to compress and concept of data hiding implemented using steganography.

Fig. 2: Input images (a)Elaina (b)Penguin (c)Girl (d)Cam-man (e)Monarch (f)Tree leaf (g)Lena

Fig. 3: Compressed image (a)Elaina (b)Penguin (c)Girl (d)Cam-man (e)Monarch (f)Tree leaf (g)Lena

III. EXPERIMENTAL RESULT AND ANALYSIS To maintain the advantages of SMVQ can make sure the original size if image is reduced after compression. Also we can hide the secret data in the compressed image after scrambling secret data by secret key and at receiver side extraction of secret data is achieved successfully. The procedures for hiding and extracting and reversing are straightforward. The compression ratio CR can be calculated as-

509

Synchronously Image Size Reduction using Modified Vector Quantization with Data Hiding (IJIRST/ Volume 3 / Issue 02/ 082)

CR [1] =

8×M×N Lc

Where, M and N are size of image And Lc is length of compressed code. Parameters/images Original size of image in (kb) Compression ratio Size after compression in (kb) Data hide

Table - 1 Evaluation of various parameters for various images Elaina Penguin Girl Cam-man Monarch 121 109 84.7 65 549 23.19 25.95 25.86 24.40 20.48 4.42 4.87 3.83 4.41 4.91 Hello Hello Hello Hello Hello Elaina penguin girl camera monarch

Tree leaf 197 17.34 5.6 Green leaf town

Lena 500 28.94 4.56 3523 lena

IV. CONCLUSION We propose an image size reduction and data hiding scheme based on SMVQ which has the flexibility in adjusting the data hiding size. The flexibility makes the proposed scheme suitable for more applications such as transmission of data over networks, security and also less storage. We got admirable results with side match vector quantization technique and goal to reduce the size of image is proved. In the future, we will try to design a decompression algorithm to reconstruct the original image. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

Chuan Qin, Chin-Chen Chang, Fellow, IEEE, and Yi-Ping Chiu,” A Novel Joint Data-Hiding and Compression Scheme Based on SMVQ and Image Inpainting,” IEEE transactions on image processing, VOL. 23, NO. 3, MARCH 2014 P. Tsai, “Histogram-based reversible data hiding for vector quantization compressed images,” IET Image Process., vol. 3, no. 2, pp. 100–114,2009. C. C. Chen and C. C. Chang, “High capacity SMVQ-based hiding scheme using adaptive index,” Signal Process., vol. 90, no. 7, pp. 2141–2149, 2010. C. C. Chang, W. L. Tai, and C. C. Lin, “A reversible data hiding scheme based on side match vector quantization,” IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 10, pp. 1301–1308, Oct. 2006. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, Apr. 2004. T. Kim, “Side match and overlap match vector quantizers for images,”IEEE Trans. Image Process., vol. 1, no. 2, pp. 170–185, Apr. 1992. R. M. Gray, “Vector quantization,” IEEE Acoust., Speech, Signal Process., vol. 1, pp. 4–29, 1984. W. C. Du and W. J. Hsu, “Adaptive data hiding based on VQ compressed images,” IEE Proc. Vis., Image Signal Process., vol. 150, no. 4,pp. 233–238, Aug. 2003. C. H. Hsieh and J. C. Tsai, “Lossless compression of VQ index with search-order coding,” IEEE Trans. Image Process., vol. 5, no. 11,pp. 1579–1582, Nov. 1996. Z. X. Qian and X. P. Zhang, “Lossless data hiding in JPEG bitstream,”J. Syst. Softw., vol. 85, no. 2, pp. 309–313, 2012. Z. C. Ni, Y. Q. Shi, N. Ansari, and W. Su, “Reversible data hiding,”IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 3, pp. 354–362,Mar. 2006. www.wikipedia.org www.google.co.in

510

Turn static files into dynamic content formats.

Create a flipbook

Synchronously Image Size Reduction using Modified Vector Quantization with Data Hiding

Published on Nov 7, 2016

IJIRST

This paper represents the scheme of data hiding and image size reduction based on side match vector quantization (SMVQ). On the sender side, residual blocks of an image compressed consecutively by SMVQ. Except for the blocks in the leftmost and topmost of the image, each of the other residual blocks is embedded with secret data. As the scheme of data compression is followed by data hiding technique. The goal of technique is to reduce the size of original image and to save storage. Compressed image is output of scheme which can be decompressed to get original image. It can be reused for various applications. Experimental results demonstrate the effectiveness of the proposed scheme in terms of compression ratio.