Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Volume No. 2 Issue No.5, December 2014 5 IMAGE TRANSFORMATION & DWT BASED IMAGE DECOMPOSITION FOR COVERT COMMUNICATION Ada Rao 1 , Dinesh Goyal 2 , Vipin Khandelwal 3 1 Research Scholar, SGVU, Jaipur, Rajasthan 2 Associate Professor, SGVU, Jaipur, Rajasthan 3 Assistant Professor, GIT, Jaipur, Rajasthan 1 raoada16@gmail.com, 2 dgoyal@gyanvihar.org, 3 vipin677@gmail.com ABSTRACT Widely used computer, and therefore require large-scale data storage and transfer, and efficient method of data storage has become necessary. Image compression is to reduce the number of bytes in an image file, without degrading the image quality to an unacceptable level. In reducing the file size to allow more images to be stored in a given amount of memory or disk space. It also reduces the desired image is transmitted from a website via the Internet or downloaded time. Gray image is 256 × 256 pixels of 65,536 Yuan, to store and a typical 640 × 480 colour image of nearly one million. These files are downloaded from the Internet can be very time-consuming task. A significant portion of the image data of the multimedia data comprises they occupy the major portion of the communication bandwidth used for the multimedia communication. Therefore, the development of effective techniques for image compression has become quite necessary [9]. The basic goal of image compression is to find the image representation associated with fewer pixels. Two basic principles used in image compression is redundant and irrelevant. Source Redundancy eliminating redundant and irrelevant omit pixel values rather than by the human eye to detect. International standard for image compression work began in the late 1970s with the CCITT (now ITU-T) requires specification of the binary image compression algorithm facsimile communications. Image compression standard brings many benefits, such as: (1) different image files between devices and applications easily exchanged; (2) the re-use of existing hardware and software products are more widely; (3) the existence of benchmarks and benchmark data sets for new and alternative development. KEYWORDS- Image Transformation, Compression, Discrete Wavelet Transformation, DCT, Image Fragmentation. INTRODUCTION Image decomposition is used specially for the compression of images where tolerable degradation is required. With the wide use of computers and consequently need for large scale storage and transmission of data, efficient ways of storing of data have become necessary. With the growth of technology and entrance into the Digital Age, the world has found itself amid a vast amount of information. Dealing with such enormous information can often present difficulties. 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. These image files can be very large and can occupy a lot of memory. A gray scale image that is 256 x 256 pixels have 65, 536 elements to store and a typical 640 x 480 color image have nearly a million. Downloading of these files from internet can be very time consuming task. Image data comprise of a significant portion of the multimedia data and they occupy the major portion of the communication bandwidth for multimedia communication. Therefore development of efficient techniques for image compression has become quite necessary [9]. A common characteristic of most images is that the neighboring pixels are highly correlated and therefore contain highly redundant information. The basic objective of image compression is to find an image representation in which pixels are less correlated. The two fundamental principles used in image compression are redundancy and irrelevancy. Redundancy removes redundancy from the signal source and irrelevancy omits pixel values which are not noticeable by human eye. JPEG and JPEG 2000 are two important techniques used for image compression. 1.1 Image Transformations Image transformations typically involve the manipulation of multiple bands of data, whether from a single multispectral image or from two or more images of the same area acquired at different times (i.e. multi temporal image data). Either way, image transformations generate "new" images from two or more sources which highlight particular features or properties of interest, better than the original input images.