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dc.contributor.authorTemmar, Mohamed Riadh-
dc.contributor.otherBerrani, Sid-Ahmed, Directeur de thèse-
dc.contributor.otherDugelay, Jean-Luc, Directeur de thèse-
dc.date.accessioned2023-10-09T09:24:18Z-
dc.date.available2023-10-09T09:24:18Z-
dc.date.issued2023-
dc.identifier.otherEP00539-
dc.identifier.urihttp://repository.enp.edu.dz/jspui/handle/123456789/10781-
dc.descriptionMémoire de Projet de Fin d’Études : Electronique : Alger, École Nationale Polytechnique : 2023fr_FR
dc.description.abstractImage compression plays a vital role in storing and transmitting digital media. In addition to traditional compression methods, there have been recent advancements in AI-based techniques. These methods are designed with specific objectives in mind, such as optimized image reconstruction or utilizing latent representations for computer vision tasks. In this study, we explore the variations among these AI-based codecs based on their objectives by tackling a classification problem. following that we focuses on creating an enhanced image compressor capable of performing three tasks: image compression, computer vision, and image processing. Specifically, we chose face recognition and resolution doubling as secondary tasks alongside image compression.fr_FR
dc.language.isoenfr_FR
dc.subjectArtificial intelligencefr_FR
dc.subjectImage compressionfr_FR
dc.subjectFace recognitionfr_FR
dc.subjectImage processingfr_FR
dc.subjectComputer visionfr_FR
dc.titleThe impact of the new image compression scheme JPEG AI on image analysis tasksfr_FR
dc.typeThesisfr_FR
Collection(s) :Département Electronique

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