COMPRESSION D’IMAGES FIXES BIOMEDICALES PAR TRANSFORMEE EN ONDELETTES, QUANTIFICATION VECTORIELLE ET CODAGE ENTROPIQUE

Dans ce travail, nous nous intéressons à la compression d’images biomédicales fixes par différents types de latransformée en ondelettes discrètes, associés à différents algorithmes de quantification vectorielle et de codageentropique.Ce type de compression nous a permis de déterminer la qualité des images reconstruites (PSNR) et le taux decompression (TC) correspondants selon le type de l’ondelette et les algorithmes de QV et de codage entropiqueutilisés.Une étude comparative a été menée dans le but de déterminer les méthodes conduisant aux meilleurs résultatspossibles

STUDY OF EFFECT OF FILTERS AND DECOMPOSITION LEVEL IN WAVELET IMAGE COMPRESSION

In this paper, we introduce a compression algorithm using wavelet transform. The principle of wavelet transform is todecompose hierarchically the input image into a series of successively lower resolution reference images and detail imageswhich contain the information needed to be reconstructed back to the next higher resolution level .The histogram of image sub-bands provides us with information on the distribution of the coefficient values in this subimage.The sub-band images resulting from wavelet transform are not of equal significance. Some sub-bands contain moreinformation than others. The total number of available bits describing an image is however inevitably limited. Therefore, it isdesirable to allocate more bits to those sub-bands images which can be coded more accurately than others. The objective of asuch bit allocation method is to optimize the overall coder performance and minimize the quantization error. In determiningwhich wavelet filter is to be used for image compression, some of the properties considered are vanishing moments. The phasenon-linearity of the filter can cause severe degradation in the subjective quality of an image. It is related to the symmetry of thefilter coefficients. The wavelet transform is implemented using a linear-phase Biorthogonal filter with four levels ofdecomposition.For this study, we use a scalar quantization with uniform threshold quantizers. The quantization method is PCM (pulsecoded modulation) for the coefficients in all high-pass sub-bands. The coefficients of low-pass sub-bands are DPCM(Differential PCM) quantized per region

NEW VIDEO COMPRESSION USING MSPIHT3D

ABSTRACTIn this paper, we propose a new approach to video compression based on the principle of Set Partitioning In Hierarchical Treealgorithm (SPIHT). Our approach, the modified SPIHT3D (MSPIHT3D), distributes entropy differently than SPIHT3D andalso optimizes the coding. This approach can produce results that are a significant improvement on the Peak Signal-to-NoiseRatio (PSNR) and compression ratio obtained by SPIHT3D algorithm, without affecting the computing time.KEYWORDS: video compression,, MSPIHT3D, arithmetic Coding, PSNR, Compression ratio

Machine learning models to predict rare earth elements distribution in Tethyan phosphate ore deposits: Geochemical and depositional environment implications

The global market for rare earth elements (REE) is growing rapidly, driven by rising demand and limited production sources, prompting interest in recovering REE from secondary sources such as phosphate deposits. The Tethyan belt, extending across North Africa and the Middle East contains substantial Upper Cretaceous to Eocene REE-rich phosphorite deposits but with limited geochemical data available. This study provides a novel machine-learning (ML) method to predict REE contents in these deposits and verify a useful geochemical classification based on the concentrations of nine major element oxides. Four ML models are developed to achieve this: eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Regression (SVR), and Decision Tree (DT). The datasets are divided geochemically into oxic and sub-oxic patterns and these are evaluated with the ML models separately and in combination to accurately predict light REE (LREE), heavy REE (HREE), and total REE contents (∑REE). For the oxic pattern dataset, Fe2O3 and K2O exhibit the highest feature importance consistent with a glauconite influence. For the sub-oxic pattern dataset, MnO and SiO2 exhibit the highest feature importance consistent with high terrigenous inputs (MnO), and silicification. The ML results support the importance of the local deposition environment in determining REE distributions in these deposits. Paleogeography, ocean-margin tectonics, sea-level oscillations, and marine currents exert influence on the local depositional environments. The eXtreme Gradient Boosting model generates the lowest REE prediction errors for all the datasets evaluated

Private and Secure Public-Key Distance Bounding: Application to NFC Payment

Distance-Bounding is used to defeat relay attacks. For wireless payment systems, the payment terminal is not always online. So, the protocol must rely on a public key for the prover (payer). We propose a generic transformation of a (weakly secure) symmetric distance bounding protocol which has no post-verification into wide-strong-private and secure public-key distance bounding

The Local Forking Lemma and its Application to Deterministic Encryption

We bypass impossibility results for the deterministic encryption of public-key-dependent messages, showing that, in this setting, the classical Encrypt-with-Hash scheme provides message-recovery security, across a broad range of message distributions. The proof relies on a new variant of the forking lemma in which the random oracle is reprogrammed on just a single fork point rather than on all points past the fork

ETUDE DE LA TRANSFORMEE EN ONDELETTES DANS LA COMPRESSION D’IMAGES FIXES

La Transformée en Ondelettes est devenue en quelques années un sujet de recherche très débattu. On ne compte plusaujourd'hui les applications qui utilisent cette technique. Il s'agit d'un algorithme permettant de calculer une représentation d'unsignal en bandes de fréquences indépendantes. Cette représentation est particulièrement utile pour le traitement d'images.Dans ce travail, on étudie les principales caractéristiques des ondelettes qui influent sur la compression d’image. On utilise latransformée en ondelettes discrètes (DWT) pour décomposer des images biomédicales fixes ; ensuite, on applique unequantification vectorielle et scalaire, puis un codage entropique. Cette étude nous a permis de déterminer les méthodesconduisant aux meilleurs résultats possible