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Exploitation des statistiques structurelles d'une image pour la prédiction de la saillance visuelle et de la qualité perçue

Abstract : 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.
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Michael Nauge. Exploitation des statistiques structurelles d'une image pour la prédiction de la saillance visuelle et de la qualité perçue. Traitement des images [eess.IV]. Université de Poitiers, 2012. Français. ⟨NNT : 2012POIT2300⟩. ⟨tel-03675653⟩

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