Абстрактный

Compression ? Innovated Using Wavelets in Image

P.Kavitha

In this technological field, image storage is happening through the personal computer. A medical doctor can make a diagnosis using a full three – dimensional image on a computer screen – not long ago surgery would have been necessary to capture the same critical point of view. Satellite images of earth and places beyond is been continually transmitted over communication channels. The Internet – still in its childhood – continues to flourish and influence our personal and professional lives. Common to these and many other applications is the storage of digital imagery. The proliferation of digital media has motivated innovative methods for compressing digital images. The popular Joint Photographic Experts Group (JPEG) and Graphical Interchange Format (GIF) standards have been the prevailing methodologies in image compression in the past decade. Alternatively, recent research in digital image compression has explored and improved the utility of the wavelet transform; its success as a compression technique has prompted its inclusion in the JPEG 2000 standard. This study has three main objectives.1. To ‘compress’ an image by taking it’s wavelet representation and throwing out those coefficients whose weight was lower than some fraction of the norm.2. To use the wavelets belong to the Deslauriers- Dubuc family.3. To work with a specific kind of thresholding and basis functions for compression.The applications of many wavelet based compression schemes most widely used Daubechies wavelet family, which are symmetric biorthogonal wavelets. However, this thesis significantly presents Deslauriers - Dubuc family set of wavelets, which is also symmetric biorthogonal for the transformation of the image.The steps involved to compress an image in this paper are as follows: 1. Digitize the source image into a signal s, which is a string of numbers. 2. Decompose the signal into a sequence of wavelet coefficients W. 3. Use threshold to modify the wavelet coefficients from w to another sequence W’. 4. Use quantization to convert W’ to a sequence q. 5. Apply entropy coding to compress q into a sequence e.