Visual quality enhancement for color images in the framework of the JPEG2000 compression standard

In the last years, the consideration of different models of the Human Visual System (HVS) in the final perceived quality of the compressed images becomes a major research subject. Therefore, it is natural to take advantage of the recent knowledge on both perception and models of the human vision in an image compression system. Thus, in this paper we propose an integration method of that knowledge for the improvement of perceptual JPEG2000 image compression quality. This method consists on two parts : a laboratory evaluation of the HVS model by the Contrast Sensitivity Function (CSF), and an implementation technique of visual weightings for the JPEG2000 scheme, using the evaluated HVS model in the Fourier domain of the color image.Durant les dernières années, la prise en compte de modèles du Système Visuel Humain (SVH) dans l'évaluation de la qualité visuelle des images couleur compressées, est devenu un sujet de recherche majeur. Il semble naturel d'intégrer davantage les connaissances récentes sur la perception et la modélisation de la vision humaine, dans les systèmes de compression d'images. Ainsi, dans cet article, nous proposons une méthode d'intégration de ces connaissances pour l'augmentation de la qualité visuelle d'images compressées JPEG2000. Cette méthode consiste en deux parties : une évaluation de laboratoire pour la modélisation du SVH par la Fonction de Sensibilité au Contraste (CSF) et une technique de calcul de facteurs de pondération visuelle pour la compression JPEG2000, utilisant le modèle SVH évalué, dans le domaine de Fourier de l'image couleur

Biologically inspired feature extraction for rotation and scale tolerant pattern analysis

Biologically motivated information processing has been an important area of scientific research for decades. The central topic addressed in this dissertation is utilization of lateral inhibition and more generally, linear networks with recurrent connectivity along with complex-log conformal mapping in machine based implementations of information encoding, feature extraction and pattern recognition. The reasoning behind and method for spatially uniform implementation of inhibitory/excitatory network model in the framework of non-uniform log-polar transform is presented. For the space invariant connectivity model characterized by Topelitz-Block-Toeplitz matrix, the overall network response is obtained without matrix inverse operations providing the connection matrix generating function is bound by unity. It was shown that for the network with the inter-neuron connection function expandable in a Fourier series in polar angle, the overall network response is steerable. The decorrelating/whitening characteristics of networks with lateral inhibition are used in order to develop space invariant pre-whitening kernels specialized for specific category of input signals. These filters have extremely small memory footprint and are successfully utilized in order to improve performance of adaptive neural whitening algorithms. Finally, the method for feature extraction based on localized Independent Component Analysis (ICA) transform in log-polar domain and aided by previously developed pre-whitening filters is implemented. Since output codes produced by ICA are very sparse, a small number of non-zero coefficients was sufficient to encode input data and obtain reliable pattern recognition performance