Multi-measures fusion based on multi-objective genetic programming for full-reference image quality assessment

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Misc{journals/corr/abs-1801-06030,

• author = "Naima Merzougui and Leila Djerou",
• title = "Multi-measures fusion based on multi-objective genetic programming for full-reference image quality assessment",
• howpublished = "arXiv",
• year = "2017",
• month = "4 " # dec,
• keywords = "genetic algorithms, genetic programming, image quality assessment, multi-objective optimisation, multigene",
• URL = "http://arxiv.org/abs/1801.06030",
• size = "6 pages",
• abstract = "In this paper, we exploit the flexibility of multi-objective fitness functions, and the efficiency of the model structure selection ability of a standard genetic programming (GP) with the parameter estimation power of classical regression via multi-gene genetic programming (MGGP), to propose a new fusion technique for image quality assessment (IQA) that is called Multi-measures Fusion based on Multi-Objective Genetic Programming (MFMOGP). This technique can automatically select the most significant suitable measures, from 16 full-reference IQA measures, used in aggregation and finds weights in a weighted sum of their outputs while simultaneously optimising for both accuracy and complexity. The obtained well-performing fusion of IQA measures are evaluated on four largest publicly available image databases and compared against state-of-the-art full-reference IQA approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.",
• notes = "note 2 authors",

}

Genetic Programming entries for Naima Merzougui Leila Djerou

Citations