WG1N5315 - Response to Call for AIC evaluation methodologies and compression technologies for medical images: LAR Codec

This document presents the LAR image codec as a response to Call for AIC evaluation methodologies and compression technologies for medical images.This document describes the IETR response to the specific call for contributions of medical imaging technologies to be considered for AIC. The philosophy behind our coder is not to outperform JPEG2000 in compression; our goal is to propose an open source, royalty free, alternative image coder with integrated services. While keeping the compression performances in the same range as JPEG2000 but with lower complexity, our coder also provides services such as scalability, cryptography, data hiding, lossy to lossless compression, region of interest, free region representation and coding

Adaptive Fractal and Wavelet Image Denoising

The need for image enhancement and restoration is encountered in many practical applications. For instance, distortion due to additive white Gaussian noise (AWGN) can be caused by poor quality image acquisition, images observed in a noisy environment or noise inherent in communication channels. In this thesis, image denoising is investigated. After reviewing standard image denoising methods as applied in the spatial, frequency and wavelet domains of the noisy image, the thesis embarks on the endeavor of developing and experimenting with new image denoising methods based on fractal and wavelet transforms. In particular, three new image denoising methods are proposed: context-based wavelet thresholding, predictive fractal image denoising and fractal-wavelet image denoising. The proposed context-based thresholding strategy adopts localized hard and soft thresholding operators which take in consideration the content of an immediate neighborhood of a wavelet coefficient before thresholding it. The two fractal-based predictive schemes are based on a simple yet effective algorithm for estimating the fractal code of the original noise-free image from the noisy one. From this predicted code, one can then reconstruct a fractally denoised estimate of the original image. This fractal-based denoising algorithm can be applied in the pixel and the wavelet domains of the noisy image using standard fractal and fractal-wavelet schemes, respectively. Furthermore, the cycle spinning idea was implemented in order to enhance the quality of the fractally denoised estimates. Experimental results show that the proposed image denoising methods are competitive, or sometimes even compare favorably with the existing image denoising techniques reviewed in the thesis. This work broadens the application scope of fractal transforms, which have been used mainly for image coding and compression purposes

Compression image sharing using DCT- Wavelet transform and coding by Blackely method

The increased use of computer and internet had been related to the wide use of multimedia information. The requirement forprotecting this information has risen dramatically. To prevent the confidential information from being tampered with, one needs toapply some cryptographic techniques. Most of cryptographic strategies have one similar weak point that is the information is centralized.To overcome this drawback the secret sharing was introduced. It’s a technique to distribute a secret among a group of members, suchthat every member owns a share of the secret; but only a particular combination of shares could reveal the secret. Individual sharesreveal nothing about the secret. The major challenge faces image secret sharing is the shadow size; that's the complete size of the lowestneeded of shares for revealing is greater than the original secret file. So the core of this work is to use different transform codingstrategies in order to get as much as possible the smallest share size. In this paper Compressive Sharing System for Images UsingTransform Coding and Blackely Method based on transform coding illustration are introduced. The introduced compressive secretsharing scheme using an appropriate transform (Discrete cosine transform and Wavelet) are applied to de-correlate the image samples,then feeding the output (i.e., compressed image data) to the diffusion scheme which is applied to remove any statistical redundancy orbits of important attribute that will exist within the compressed stream and in the last the (k, n) threshold secret sharing scheme, where nis the number of generated shares and k is the minimum needed shares for revealing. For making a certain high security level, eachproduced share is passed through stream ciphering depends on an individual encryption key belongs to the shareholder

3D coding tools final report

Livrable D4.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.3 du projet. Son titre : 3D coding tools final repor