Multi-gene Genetic Programming based Predictive Models for Full-reference Image Quality Assessment

Created by W.Langdon from gp-bibliography.bib Revision:1.8792

@InProceedings{DBLP:conf/iqsp/MerzouguiD22,

• author = "Naima Merzougui and Leila Djerou",
• editor = "Mohamed-Chaker Larabi and Mylene C. Q. Farias",
• title = "Multi-gene Genetic Programming based Predictive Models for Full-reference Image Quality Assessment",
• booktitle = "Image Quality and System Performance XIX, IQSP 2022",
• pages = "1--13",
• publisher = "Society for Imaging Science and Technology",
• year = "2022",
• address = "online",
• month = jan # " 15-26",
• keywords = "genetic algorithms, genetic programming",
• timestamp = "Wed, 19 Jul 2023 17:21:16 +0200",
• biburl = "https://dblp.org/rec/conf/iqsp/MerzouguiD22.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• URL = "https://www.imaging.org/IST/Conferences/EI/EI_2022/Conference/C_IQSP.aspx?#IQSP-384",
• URL = "https://doi.org/10.2352/J.ImagingSci.Technol.2021.65.6.060409",
• DOI = "10.2352/J.IMAGINGSCI.TECHNOL.2021.65.6.060409",
• abstract = "... a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets, by the combination of objective scores of a set of image quality metrics (IQM). By learning from image da-tasets, the MGGP can determine the appropriate image quality metrics, from 21 used metrics, whose objective scores employed as predictors, in the symbolic regression model, by optimizing simultaneously two competing objectives of model 'goodness of fit' to data and model 'complexity'. Six largest publicly available image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.",
• notes = "DOI gives journal jist1210 version \cite{merzougui:2021:JIST}

  IQSP-384

  University of Biskra, Algeria",

}

Genetic Programming entries for Naima Merzougui Leila Djerou

Citations