Full-Reference Image Quality Expression via Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{DBLP:journals/tip/BakurovBSCV23,
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author = "Illya Bakurov and Marco Buzzelli and
Raimondo Schettini and Mauro Castelli and Leonardo Vanneschi",
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title = "Full-Reference Image Quality Expression via Genetic
Programming",
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journal = "{IEEE} Trans. Image Process.",
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volume = "32",
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pages = "1458--1473",
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year = "2023",
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keywords = "genetic algorithms, genetic programming",
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URL = "https://doi.org/10.1109/TIP.2023.3244662",
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DOI = "doi:10.1109/TIP.2023.3244662",
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timestamp = "Sat, 11 Mar 2023 00:00:00 +0100",
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biburl = "https://dblp.org/rec/journals/tip/BakurovBSCV23.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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abstract = "Full-reference image quality measures are a
fundamental tool to approximate the human visual system
in various applications for digital data management:
from retrieval to compression to detection of
unauthorized uses. Inspired by both the effectiveness
and the simplicity of hand-crafted Structural
Similarity Index Measure (SSIM), in this work, we
present a framework for the formulation of SSIM-like
image quality measures through genetic programming. We
explore different terminal sets, defined from the
building blocks of structural similarity at different
levels of abstraction, and we propose a two-stage
genetic optimization that exploits hoist mutation to
constrain the complexity of the solutions. Our
optimized measures are selected through a cross-dataset
validation procedure, which results in superior
performance against different versions of structural
similarity, measured as correlation with human mean
opinion scores. We also demonstrate how, by tuning on
specific datasets, it is possible to obtain solutions
that are competitive with (or even outperform) more
complex image quality measures.",
- }
Genetic Programming entries for
Illya Bakurov
Marco Buzzelli
Raimondo Schettini
Mauro Castelli
Leonardo Vanneschi
Citations