Contrôle des performances et conciliation d’erreurs dans les décodeurs d’image

Abstract : This thesis deals with the development and implementation of error detection and correction algorithms in images, in order to control the quality of produced images at the output of digital decoders. To achieve the objectives of this work, we first study the state-of the-art of the existing approaches. Examination of classically used approaches justified the study of a set of objective methods for evaluating the visual quality of images, based on machine learning methods. These algorithms take as inputs a set of characteristics or metrics extracted from the images. Depending on the characteristics extracted from the images, and the availability or not of a reference image, two kinds of objective evaluation methods have been developed: the first based on full reference metrics, and the second based on no-reference metrics; both of them with non-specific distortions. In addition to these objective evaluation methods, a method of evaluating and improving the quality of the images based on the detection and correction of the defective pixels in the images has been implemented. The proposed results have contributed to refining visual image quality assessment methods as well as the construction of objective algorithms for detecting and correcting defective pixels compared to the various currently used methods. An implementation on an FPGA has been carried out to integrate the models with the best performances during the simulation phase.
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Ghislain Takam Tchendjou. Contrôle des performances et conciliation d’erreurs dans les décodeurs d’image. Micro et nanotechnologies/Microélectronique. Université Grenoble Alpes, 2018. Français. ⟨NNT : 2018GREAT107⟩. ⟨tel-02140199⟩

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