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Referenceless Image Quality Assessment Utilizing Deep Transfer-Learned Features

  • Identification data

    Identifier: imarina:9385563
    Handle: https://hdl.handle.net/20.500.11797/imarina9385563
    Authors:
    Ahmed, BasmaOmer, Osama ARashed, AmalPuig, DomenecAbdel-Nasser, Mohamed
    Abstract:
    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.

  • Others:

    Author, as appears in the article.: Ahmed, Basma; Omer, Osama A; Rashed, Amal; Puig, Domenec; Abdel-Nasser, Mohamed
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Keywords: Blind image quality Deep learnin Deep learning Pseudo-reference Similarity measures
    Abstract: 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.
    Thematic Areas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: domenec.puig@urv.cat mohamed.abdelnasser@urv.cat
    Author identifier: 0000-0002-0562-4205 0000-0002-1074-2441
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://ebooks.iospress.nl/doi/10.3233/FAIA220345
    Papper original source: Frontiers In Artificial Intelligence And Applications. 356 243-248
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.3233/FAIA220345
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Proceedings Paper

  • Keywords:

    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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