Adaptive Speech Compression Based on Discrete Wave Atoms Transform

This paper proposes a new adaptive speech compression system based on discrete wave atoms transform. First, the signal is decomposed on wave atoms, then wave atom coefficients are truncated using a new adaptive thresholding which depends on the SNR estimation. The thresholded coefficients are quantized using Max Lloyd scalar quantizer. Besides, they are encoded using zero run length encoding followed by Huffman coding. Numerous simulations are performed to prove the robustness of our approach. The results of current work are compared with wavelet based compression by using objective criteria, namely CR, SNR, PSNR and NRMSE. This study shows that the wave atoms transform is more appropriate than wavelets transform since it offers a higher compression ratio and a better speech quality

Compression -Innovated Using Wavelets in Image

ABSTRAC: 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

Wavelet-based image compression for mobile applications.

The transmission of digital colour images is rapidly becoming popular on mobile telephones, Personal Digital Assistant (PDA) technology and other wireless based image services. However, transmitting digital colour images via mobile devices is badly affected by low air bandwidth. Advances in communications Channels (example 3G communication network) go some way to addressing this problem but the rapid increase in traffic and demand for ever better quality images, means that effective data compression techniques are essential for transmitting and storing digital images. The main objective of this thesis is to offer a novel image compression technique that can help to overcome the bandwidth problem. This thesis has investigated and implemented three different wavelet-based compression schemes with a focus on a suitable compression method for mobile applications. The first described algorithm is a dual wavelet compression algorithm, which is a modified conventional wavelet compression method. The algorithm uses different wavelet filters to decompose the luminance and chrominance components separately. In addition, different levels of decomposition can also be applied to each component separately. The second algorithm is segmented wavelet-based, which segments an image into its smooth and nonsmooth parts. Different wavelet filters are then applied to the segmented parts of the image. Finally, the third algorithm is the hybrid wavelet-based compression System (HWCS), where the subject of interest is cropped and is then compressed using a wavelet-based method. The details of the background are reduced by averaging it and sending the background separately from the compressed subject of interest. The final image is reconstructed by replacing the averaged background image pixels with the compressed cropped image. For each algorithm the experimental results presented in this thesis clearly demonstrated that encoder output can be effectively reduced while maintaining an acceptable image visual quality particularly when compared to a conventional wavelet-based compression scheme