Volume 8, No. 3, March – April 2017 ISSN No. 0976-5697 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info Lossless Compression of VC Shares in RGB Color Space M. Mary Shanthi Rani G.Germine Mary Department of Computer Science and Applications Department of Computer Science Gandhigram Rural Institute – Deemed University, Dindigul, Fatima College, India Madurai, India Abstract: Visual Cryptography is a special encryption technique to hide information in images in such a way that it can be decrypted by the human vision if the correct key image is used. In Visual Cryptography the reconstructed image after decryption process encounters a major problem of Pixel expansion. This is overcome in this proposed method by minimizing the memory size using lossless image compression techniques. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages. Hybrid techniques are used in this proposed method as it can exploit multiple kinds of redundant information. Keywords: Visual Cryptography; HVS; Image Compression; Vector Quantization; Run Length Encoding; Huffman Coding I. INTRODUCTION VC schemes conceal the secret image into two or more images which are called shares. The secret image can be In today’s high technology environment, organizations are recovered simply by stacking the shares together. Each of the becoming increasingly reliant on their information systems. shares looks like a group of random pixels and of course, The public is concerned more and more about the proper use looks meaningless by itself [3]. Naturally, any single share, of information, particularly personal data. The threats to before being stacked up with the others, reveals nothing about information systems from criminals and terrorists are on the the secret image and hence are very safe. This way, the rise. Headline news scares about stolen or misplaced data are security level of the secret image when transmitted via the becoming an everyday occurrence. So it is important to Internet can be efficiently increased. protect information and its secrecy. Addressing privacy and In this secret image sharing scheme, the size of the shares security concerns requires a combination of technical, social, is 4 times the size of the original image due to pixel and legal approaches. expansion. This disadvantage of VC can be overcome by Cryptography is the practice and study of techniques image compression before it is shared over the network. for secure communication in the presence of adversaries. Image Compression is the process of reducing the number Cryptography refers to encryption, which is the process of of bits required to represent an image[4]. Compression has converting ordinary information called plaintext into traditionally been done with little regard for image processing unintelligible text called cipher text. Decryption is the operations that may precede or follow the compression steps. reverse, in other words, moving from the unintelligible In this proposed scheme compression follows VC as it results ciphertext back to plaintext. [1]. in pixel expansion. Data compression is the mapping of a Visual Cryptography (VC) is a cryptographic technique data set into a bit stream to decrease the number of bits which allows visual information (pictures, text, etc.) to be required to represent the data set. With data compression, one encrypted in such a way that decryption is done by Human can store more information in a given storage space and Visual System. One of the best-known techniques has been transmit information faster over communication channels. developed by Moni Naor and Adi Shamir in 1994 [2]. Thus, if data can effectively be compressed wherever Modern cryptography including VC has four objectives: possible, significant improvements of data throughput can be • Confidentiality - the information cannot be achieved. In some instances, file sizes can be reduced by up to understood by anyone except for whom it was 60-70 %. Many files can be combined into one compressed intended document making sending easier, provided combined file size is not huge. Strategies for Compression are reducing redundancies and exploiting the characteristics of human vision [5]. The two • Integrity - the information cannot be altered in types of data compressions are lossless and lossy. Lossless storage or transit between sender and intended compression has an advantage that the original information receiver can be recovered exactly from the compressed data. The proposed system performs lossless compression using hybrid • Non-repudiation - the sender of the information techniques of Vector Quantization (VQ), Run Length cannot deny at a later stage his intentions in the Encoding (RLE), Huffman Encoding (HE) and Predictive transmission of the information Coding. The principal approach in data compression is the • Authentication - the sender and receiver can confirm reduction of the amount of image data (bits) while each other’s identity and the origin/destination of the preserving information (image details). information © 2015-19, IJARCS All Rights Reserved 79 Mary Shanthi et al, International Journal of Advanced Research in Computer Science, 8 (3), March-April 2017,79-85 The main objectives of this proposed method are to formulate a secret sharing system which has the following characteristics. • Exploit the advantage of VC to create meaningless shares to hide secret • Minimizing the drawback of VC, that is image expansion, by employing multistage hybrid Image Compression techniques The entire paper is organized in the following sequence. Section 2 various types of data redundancies and compression techniques used in this paper are precisely outlined , section 3 Methods of compression are explained, In section 4 results Figure 1. Vector Quantization Scheme. are presented and discussed. Lastly, section 5 includes conclusions. To reduce psycho-visual redundancy we use Quantizer. The elimination of psychovisually redundant data may result II. REVIEW OF COMPRESSION TECHNIQUES in a loss of quantitative information. It is commonly referred to as quantization. Quantization is a many-to-one mapping The necessity for efficient image compression techniques that replaces a set of values with only one representative is ever increasing since the original images need large value. Scalar and vector quantization are two basic types of amounts of disk space, which is a huge disadvantage during quantization. Scalar quantization (SQ) performs many-to-one transmission & storage. Though a lot of compression mapping on each value. Vector Quantization (VQ) replaces techniques already exist, a better technique which is faster, each block of input pixels with the index of a vector in the memory efficient and that suits the requirements of the user is codebook, which is close to the input vector by using some under investigation. In this paper, a Lossless method of image closeness measurements (Fig. 1.). The decoder simply compression and decompression is proposed using a hybrid receives each index and looks up the equivalent vector in the coding algorithm with higher performance for the special type codebook [6-8]. of Visual Cryptography images. The proposed technique is Another important form of data redundancy is interpixel simple in implementation and utilizes less memory. A redundancy, which is directly related to the inter-pixel software algorithm has been developed and implemented to correlations within an image. Because the value of any given compress and decompress the given image using three pixel can be reasonably predicted from the value of its different hybrid techniques in MATLAB platform. neighbors, the information carried by individual pixels is During the past three decades, various compression relatively small. Once the correlation between the pixels is methods have been developed to address major challenges reduced, we can take advantage of the statistical faced by digital imaging. These compression methods can be characteristics and the variable length coding theory to reduce classified broadly into lossy or lossless compression. Lossy the storage quantity. This is the most important part of the compression can achieve a high compression ratio, 50:1 or image compression algorithm; there are a lot of relevant higher since it allows some acceptable degradation. Yet it processing methods available. The best-known methods are cannot completely recover the original data. On the other Predictive Coding and Run Length Encoding (RLE). Both are hand, lossless compression can completely recover the lossless coding methods, which mean that the decoded image original data [4]. and the original image have the same value for every Data compression is defined as the process of encoding corresponding element [6,9]. data using a representation that reduces the overall size of Run length coding replaces data by a (length, value) pair, data. This reduction is possible when the original dataset where “value” is the repeated value and “length” is the contains some type of redundancy. The basis of the number of repetitions (Fig. 2). This technique is especially compression process is the removal of redundant data. Digital successful in compressing bi-level images since