As there is a rapid growth in the field of wireless communications, the demand for various multimedia services is also increasing. The data that is being transmitted suffers from distortions through source encoding and transmission over errorprone channels. Due to these errors, the quality of the content is degraded. There is a need for service providers to provide certain Quality of Experience (QoE) to the end user. Several methods are being developed by network providers for better QoE.The human tendency mainly focuses on distortions in the Region of Interest(ROI) which are perceived to be more annoying compared to the Background(BG). With this as a base, the main aim of this thesis is to get an accurate prediction quality metric to measure the quality of the image over ROI and the BG independently. Reduced Reference Image Quality Assessment (RRIQA), a reduced reference image quality assessment metric, is chosen for this purpose. In this method, only partial information about the reference image is available to assess the quality. The quality metric is measured independently over ROI and BG. Finally the metric estimated over ROI and BG are pooled together to get aROI aware metric to predict the Mean Opinion Score (MOS) of the image.In this thesis, an ROI aware quality metric is used to measure the quality of distorted images that are generated using a wireless channel. The MOS of distorted images are obtained. Finally, the obtained MOS are validated with the MOS obtained from a database [1].It is observed that the proposed image quality assessment method provides better results compared to the traditional approach. It also gives a better performance over a wide variety of distortions. The obtained results show that the impairments in ROI are perceived to be more annoying when compared to the BG.

Dans le domaine de la vision par ordinateur l'utilisation de points d'intérêt (PI) est récurrente pour les problématiques de reconnaissance et de suivi d'objets. Plusieurs études ont prouvé l'utilité de ces techniques, associant robustesse et un temps de calcul pouvant être compatible avec le temps réel. Cette thèse propose d'étudier et d'exploiter ces descripteurs de statistiques de l'image sous un tout autre regard. Ainsi, nous avons menée une étude sur le lien entre les PI et la saillance visuelle humaine. De cette étude nous avons pu développer une méthode de prédiction de carte de saillance exploitant la rapidité d'exécution de ces détecteurs. Nous avons également exploité le pouvoir descriptif de ces PI afin de développer de nouvelles métriques de qualité d'images. Grâce à des résultats encourageant en terme de prédiction de qualité perçue et la faible quantité d'information utilisée, nous avons pu intégrer notre métrique "QIP" dans une chaîne de transmission d'images sur réseau sans fil de type MIMO. L'ajout de cette métrique permet d'augmenter la qualité d'expérience en garantissant la meilleure qualité malgré les erreurs introduites par la transmission sans fil. Nous avons étendu cette étude, par l'analyse fine des statistiques structurelles de l'image et des migrations d'attributs afin de proposer un modèle générique de prédiction des dégradations. Enfin, nous avons été amenés à conduire diverses expériences psychovisuelles, pour valider les approches proposées ou dans le cadre de la normalisation de nouveaux standards du comité JPEG. Ce qui a mené à développer une application web dédiée à l'utilisation et la comparaison des métriques de qualité d'images. / In the field of computer vision, the use of interest points (IP) is very frequent for objects tracking and recognition. Several studies have demonstrated the usefulness of these techniques, combining robustness and complexity that can be compatible with the real time. This thesis proposes to explore and exploit these image statistical descriptors under a different angle. Thus, we conducted a study on the relationship between IP and human visual saliency. In this study, we developed a method for predicting saliency maps relying on the efficiency of the descriptors. We also used the descriptive power of the PI to develop new metrics for image quality. With encouraging results in terms of prediction of perceived quality and the reduced amount of used information, we were able to integrate our metric "QIP" in an image transmission framework over a MIMO wireless network. The inclusion of this metric can improve the quality of experience by ensuring the best visual quality despite the errors introduced by the wireless transmission. We have extended this study by deeply analyzing structural statistics of the image and migration attributes to provide a generic model for predicting impairments. Finally, we conducted various psychovisual experiments to validate the proposed approaches or to contribute to JPEG standard committee. This led to develop a web application dedicated to the benchmark of image quality metrics.