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Genetic Programming for Screen Content Image Quality Assessment

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Artificial Intelligence: Theories and Applications (ICAITA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1769))

Abstract

The study of Screen Content Image Quality Assessment (SCI-QA) is a new and interesting topic due to its excellent potential for instruction and optimization in various processing systems, it has been attractive recently. In this paper, we proposed a full reference quality assessment method for screen content images named Genetic Programming based Screen Content Image Quality (GP-SCIQ). The proposed method operates via a symbolic regression technique using Genetic Programming (GP). Hence, for predicting subject scores of images in datasets we combined the objective scores of a set of Image Quality Metrics (IQM). Two largest publicly available image databases (namely SICAD and SCID) are used for training and testing the predictive models, according the k-fold-cross-validation strategy. The performance of the proposed approach is evaluated, and several experiments are carried using four performance indices (SRCC, PCC, KROCC and RMSE). The results achieve superior performance to state-of-the-art methods in predicting the perceptual quality of SCIs.

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Author information

Authors and Affiliations

  1. LESIA Laboratory, University Mohamed Khider, Biskra, Algeria

    Naima Merzougui & Leila Djerou

  2. Department of Computer Science and Information Technologies, University Kasdi Merbah, Ouargla, Algeria

    Naima Merzougui

Authors
  1. Naima Merzougui
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  2. Leila Djerou
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Corresponding author

Correspondence to Naima Merzougui .

Editor information

Editors and Affiliations

  1. University of Mascara, Mascara, Algeria

    Mohammed Salem

  2. University of Granada, Granada, Spain

    Juan Julián Merelo

  3. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  4. University of Mascara, Mascara, Algeria

    Rochdi Bachir Bouiadjra

  5. University of Mascara, Mascara, Algeria

    Mohamed Debakla

  6. University of Mascara, Mascara, Algeria

    Fatima Debbat

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Merzougui, N., Djerou, L. (2023). Genetic Programming for Screen Content Image Quality Assessment. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_5

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  • DOI: https://doi.org/10.1007/978-3-031-28540-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28539-4

  • Online ISBN: 978-3-031-28540-0

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