<div> <div> <div> <p>In this thesis, two aspects of video coding improvement are discussed, namely error resilience and coding efficiency. </p> <p>With the increasing amount of videos being created and consumed, better video compression tools are needed to provide reliable and fast transmission. Many popular video coding standards such as VPx, H.26x achieve video compression by using spa- tial and temporal dependencies in the source video signal. This makes the encoded bitstream vulnerable to errors during transmission. In this thesis, we investigate an error resilient video coding for the VP9 bitstreams using error resilience packets. An error resilient packet consists of encoded keyframe contents and the prediction sig- nals for each non-keyframe. Experimental results exhibit that our proposed method is effective under typical packet loss conditions. </p> <p>In the second part of the thesis, we first present an automatic stillness feature detection method for group of pictures. The encoder adaptively chooses the coding structure for each group of pictures based on its stillness feature to optimize the coding efficiency. </p> <p>Secondly, a content-based video coding method is proposed. Modern video codecs including the newly developed AOM/AV1 utilize hybrid coding techniques to remove spatial and temporal redundancy. However, the efficient exploitation of statistical dependencies measured by a mean squared error (MSE) does not always produce the best psychovisual result. One interesting approach is to only encode visually relevant information and use a different coding method for “perceptually insignificant” regions </p> </div> </div> <div> <div> <p>xiv </p> </div> </div> </div> <div> <div> <div> <p>in the frame. In this thesis, we introduce a texture analyzer before encoding the input sequences to identify detail irrelevant texture regions in the frame using convolutional neural networks. The texture region is then reconstructed based on one set of motion parameters. We show that for many standard test sets, the proposed method achieved significant data rate reductions. </p> </div> </div> </div>

Les carottes, extraites lors des forages de puits de pétrole, font partie des éléments les plus importants dans la chaîne de caractérisation de réservoir. L’acquisition de celles-ci à travers un scanner médical permet d’étudier de façon plus fine les variations des types de dépôts. Le but de cette thèse est d’établir les liens entre les imageries scanners 3D de carottes, et les différentes propriétés pétrophysiques et géologiques. Pour cela la phase de modélisation des images, et plus particulièrement des textures, est très importante et doit fournir des descripteurs extraits qui présentent un assez haut degrés de confiance. Une des solutions envisagée pour la recherche de descripteurs a été l’étude des méthodes paramétriques permettant de valider l’analyse faite sur les textures par un processus de synthèse. Bien que ceci ne représente pas une preuve pour un lien bijectif entre textures et paramètres, cela garantit cependant au moins une confiance en ces éléments. Dans cette thèse nous présentons des méthodes et algorithmes développés pour atteindre les objectifs suivants : 1. Mettre en évidence les zones d’homogénéités sur les zones carottées. Cela se fait de façon automatique à travers de la classification et de l’apprentissage basés sur les paramètres texturaux extraits. 2. Établir les liens existants entre images scanners et les propriétés pétrophysiques de la roche. Ceci se fait par prédiction de propriétés pétrophysiques basées sur l’apprentissage des textures et des calibrations grâce aux données réelles. . / Cores extracted, during wells drilling, are essential data for reservoirs characterization. A medical scanner is used for their acquisition. This feature provide high resolution images improving the capacity of interpretation. The main goal of the thesis is to establish links between these images and petrophysical data. Then parametric texture modelling can be used to achieve this goal and should provide reliable set of descriptors. A possible solution is to focus on parametric methods allowing synthesis. Even though, this method is not a proven mathematically, it provides high confidence on set of descriptors and allows interpretation into synthetic textures. In this thesis methods and algorithms were developed to achieve the following goals : 1. Segment main representative texture zones on cores. This is achieved automatically through learning and classifying textures based on parametric model. 2. Find links between scanner images and petrophysical parameters. This is achieved though calibrating and predicting petrophysical data with images (Supervised Learning Process).