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Referenceless Image Quality Assessment Utilizing Deep Transfer-Learned Features

  • Datos identificativos

    Identificador: imarina:9385563
    Handle: https://hdl.handle.net/20.500.11797/imarina9385563
    Autores:
    Ahmed, BasmaOmer, Osama ARashed, AmalPuig, DomenecAbdel-Nasser, Mohamed
    Resumen:
    Image quality assessment (IQA) algorithms are critical for determining the quality of high-resolution photographs. This work proposes a hybrid NR IQA approach that uses deep transfer learning to enhance classic NR IQA with deep learning characteristics. Firstly, we simulate a pseudo reference image (PRI) from the input image. Then, we used a pre-trained inception-v3 deep feature extractor to generate the feature maps from the input distorted image and PRI. The distance between the feature maps of the input distorted image and PRI are measured using the local structural similarity (LSS) method. A nonlinear mapping function is used to calculate the final quality scores. When compared to previous work, the proposed method has a promising performance.

  • Otros:

    Autor según el artículo: Ahmed, Basma; Omer, Osama A; Rashed, Amal; Puig, Domenec; Abdel-Nasser, Mohamed
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Palabras clave: Blind image quality Deep learnin Deep learning Pseudo-reference Similarity measures
    Resumen: Image quality assessment (IQA) algorithms are critical for determining the quality of high-resolution photographs. This work proposes a hybrid NR IQA approach that uses deep transfer learning to enhance classic NR IQA with deep learning characteristics. Firstly, we simulate a pseudo reference image (PRI) from the input image. Then, we used a pre-trained inception-v3 deep feature extractor to generate the feature maps from the input distorted image and PRI. The distance between the feature maps of the input distorted image and PRI are measured using the local structural similarity (LSS) method. A nonlinear mapping function is used to calculate the final quality scores. When compared to previous work, the proposed method has a promising performance.
    Áreas temáticas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: domenec.puig@urv.cat mohamed.abdelnasser@urv.cat
    Identificador del autor: 0000-0002-0562-4205 0000-0002-1074-2441
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Referencia al articulo segun fuente origial: Frontiers In Artificial Intelligence And Applications. 356 243-248
    Referencia de l'ítem segons les normes APA: Ahmed, Basma; Omer, Osama A; Rashed, Amal; Puig, Domenec; Abdel-Nasser, Mohamed (2022). Referenceless Image Quality Assessment Utilizing Deep Transfer-Learned Features. Amsterdam: IOS Press
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Proceedings Paper

  • Palabras clave:

    Artificial Intelligence
    Blind image quality
    Deep learnin
    Deep learning
    Pseudo-reference
    Similarity measures
    Artificial intelligence
    Ciências agrárias i
    Comunicació i informació
    Engenharias iii
    Engenharias iv
    General o multidisciplinar
    Información y documentación
    Interdisciplinar
    Medicina ii

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