Machine learning to design full-reference image quality assessment algorithm

A crucial step in image compression is the evaluation of its performance, and more precisely, available ways to measure the quality of compressed images. In this paper, a machine learning expert, providing a quality score is proposed. This quality measure is based on a learned classification process in order to respect human observers. The proposed method namely Machine Learning-based Image Quality Measure (MLIQM) first classifies the quality using multi-Support Vector Machine (SVM) classification according to the quality scale recommended by the ITU. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). To evaluate the quality of images, a feature vector containing visual attributes describing images content is constructed. Then, a classification process is performed to provide the final quality class of the considered image. Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality. Obtained results are compared to the one obtained applying classical Full-Reference Image Quality Assessment (FR-IQA) algorithms to judge the efficiency of the proposed method.     

Optimization methodology of dynamic data structures based on genetic algorithms for multimedia embedded systems

Modern multimedia application exhibit high resource utilization. In order to efficiently run this kind of applications in embedded systems, the dynamic memory subsystem needs to be optimized. A key role in this optimization is played by the dynamic data structures that reside in every real-life application. This paper presents a novel and automated way to optimize dynamic data structures. The search space is pruned using genetic algorithms that converge to the best multilayered data structure implementation for the targeted applications.     

Stability of phytochemicals as sources of anti-inflammatory nutraceuticals in beverages — A review

Recently phytochemical constituents have attracted a lot of interest as sources of functional ingredients in food product formulations. However, before being incorporated into foods or beverages the stability of these bioactive constituents during post-harvest preservation procedures, in food matrices and during processing and storage must be considered. This review will focus on the stability of phytochemical constituents of herbs with anti-inflammatory properties, including chamomile, meadowsweet, feverfew and willow. These herbs contain a relatively high content of phenolic compounds in comparison to common fruits and vegetables. They have been traditionally used to make teas or infusions that are consumed as a remedy for pain and inflammation. Effects of preservation and extraction conditions on the level of bioactive constituents will be reviewed. The storage conditions (temperature) and thermal processing at pH levels encountered in food products will also be examined.     

Mercury speciation in the French seasonal snow cover

Snow samples have been collected in the French Alps in 1998, 1999 and 2000 in order to measure both total Hg (HgT) and reactive Hg (HgR). Concentrations of HgT were between 13 and 130 pg g(-1) and HgR concentrations were below the detection limit (approximately 0.8 pg g(-1)). Hg speciation in snow was evaluated on the basis of ionic complexation equilibrium with chloride, hydroxide, oxalate. The pH of the snow was found to be an important parameter for Hg speciation. For pH values near 3, HgC2O4 is predominant in snow samples except for snow strongly influenced by anthropogenic sources (in which case HgCl2 predominates). When pH > 4, Hg(OH)2 and HgOHCl are predominant. These latter pH values are observed for precipitation not influenced by anthropogenic sources but more by soil erosion, e.g. Saharan dusts. The knowledge of Hgr speciation in snow is a key question for understanding the mechanisms of transformation of these complexes in snow after precipitation.     

Cosmetic assessment of the human hair by confocal microscopy

The optical sectioning property of the confocal microscope offers a breakthrough from the classic observation of the hair in a scanning electron microscope (SEM). Confocal microscopy requires minimal sampling preparation, and the hair can be observed in its natural environment with less damage than by other microscopic methods such as SEM. While used in the reflection mode, the true morphology of the cuticle and the various exogenous deposits at the surface can be identified and quantified. This relatively noninvasive, nondestructive technique is routinely used by us to monitor the efficiency of cleansing shampoos, to assess the homogeneity of layering polymers, and to evaluate the changes they induce in the optical properties of the hair surface in terms of opacity, transparency, and brilliancy. A second important field of investigation uses the fluorescence channel which reveals the internal structure of the hair. Fluorescent probes (rhodamine and its derivatives) demonstrate the routes of penetration and outline the geometry of cortical cells and of the medulla according to their lipophilic or hydrophilic properties. A volume rendering of a hair cylinder provides a better understanding of the interrelationships between cuticle cells, cortical cells, and the medullar channel. This recent technology is becoming an invaluable tool for the cosmetic assessment of the hair.     

Apodized fiber Bragg gratings manufactured with the phase plate process

We present a manufacturing method based on the dynamic use of phase plates to photowrite Bragg gratings. This process allows for control of the local value of the index modulation envelope in the grating. The application to apodized fiber Bragg gratings is discussed.     

Reduction of the correlation sensitivity to the changes of the input illumination by a post processing based on the correlation statistics

Linear-correlation amplitude changes when the intensity level of the input image is modified. As recognition is often based on the correlation-peak level, a change of the input illumination may result in a false recognition. We propose an illumination-change compensation by a post processing of the correlation distribution that is based on statistical measures of the correlation histograms. The theoretical background and simulation results are provided in the frame of an actual application in biology.     

Toxic effects of Irgarol 1051 on phytoplankton and macrophytes in Lake Geneva

Irgarol 1051 is a recent herbicidal compound, inhibitor of photosynthesis, used in antifouling paints. This toxic is persistent in aquatic environments, with low abiotic and biotic degradation, highly phytotoxic, and has already been detected in estuaries and coastal areas, with suspected negative impacts on non-target organisms (aquatic plants and algae). We measured the toxicity of Irgarol 1051 to macrophytes and phytoplankton from Lake Geneva (between Switzerland and France) by determining chlorophyll fluorescence yield, and phytoplankton primary production. Long-term toxicity for phytoplankton was estimated in a microcosm study, and growth inhibition tests were performed with isolated algal strains. The concentration of Irgarol 1051 was analysed in the water, and the most polluted site showed a higher level (up to 135 ng/L) than the lowest observed effect concentration for phytoplankton (8-80 ng/L), while the macrophytes appeared to be more tolerant to Irgarol 1051 in short-term tests. The microcosm study showed that phytoplankton structure might be even more sensitive to Irgarol 1051.     

Low-dissipation and low-dispersion fourth-order Runge–Kutta algorithm

An optimized explicit low-storage fourth-order Runge–Kutta algorithm is proposed in the present work for time integration. Dispersion and dissipation of the scheme are minimized in the Fourier space over a large range of frequencies for linear operators while enforcing a wide stability range. The scheme remains of order four with nonlinear operators thanks to the low-storage algorithm. Linear and nonlinear propagation problems are finally solved to illustrate the accuracy of the present Runge–Kutta scheme.