The impact of the new image compression scheme JPEG AI on image analysis tasks

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dc.contributor.author Temmar, Mohamed Riadh
dc.contributor.other Berrani, Sid-Ahmed, Directeur de thèse
dc.contributor.other Dugelay, Jean-Luc, Directeur de thèse
dc.date.accessioned 2023-10-09T09:24:18Z
dc.date.available 2023-10-09T09:24:18Z
dc.date.issued 2023
dc.identifier.other EP00539
dc.identifier.uri http://repository.enp.edu.dz/jspui/handle/123456789/10781
dc.description Mémoire de Projet de Fin d’Études : Electronique : Alger, École Nationale Polytechnique : 2023 fr_FR
dc.description.abstract Image 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.iso en fr_FR
dc.subject Artificial intelligence fr_FR
dc.subject Image compression fr_FR
dc.subject Face recognition fr_FR
dc.subject Image processing fr_FR
dc.subject Computer vision fr_FR
dc.title The impact of the new image compression scheme JPEG AI on image analysis tasks fr_FR
dc.type Thesis fr_FR


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