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dc.contributor.author
Osorio, Felipe
dc.contributor.author
Vallejos, Ronny
dc.contributor.author
Barraza, Wilson
dc.contributor.author
Ojeda, Silvia María
dc.contributor.author
Landi, Marcos Alejandro
dc.date.available
2022-10-27T17:03:00Z
dc.date.issued
2022-06
dc.identifier.citation
Osorio, Felipe; Vallejos, Ronny; Barraza, Wilson; Ojeda, Silvia María; Landi, Marcos Alejandro; Statistical estimation of the structural similarity index for image quality assessment; Springer London Ltd; Signal, Image and Video Processing; 16; 4; 6-2022; 1035-1042
dc.identifier.issn
1863-1703
dc.identifier.uri
http://hdl.handle.net/11336/175228
dc.description.abstract
The structural similarity (SSIM) index has been studied from different perspectives in the last decade. Most of the developments consider its parameters fixed. Because each of these parameters corresponds to the weight of a factor in the final SSIM coefficient, the usual assumption that all parameters are equal to one is questionable. In this article, a new estimation method is proposed from a statistical perspective. The approach we develop is a model-based estimation method so that the usual assumption that all parameters are equal to one can be handled via approximate hypothesis-testing techniques that are properly developed in the context of regression. The method considers nonlinear models with multiplicative noise to explain the root mean square error as a function of the SSIM index. A numerical experiment based on a Monte Carlo simulation is carried out to test whether the parameters are all equal to one and to gain more insight into the performance of the estimates in practice. Our analysis showed that the assumption that the parameters are equal to one is not supported by the data and may lead to a misconception of the closeness between two images.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer London Ltd
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
HYPOTHESIS TESTING
dc.subject
NONLINEAR MODELS
dc.subject
PSEUDO-LIKELIHOOD
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STRUCTURAL SIMILARITY INDEX
dc.subject.classification
Estadística y Probabilidad
dc.subject.classification
Matemáticas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Statistical estimation of the structural similarity index for image quality assessment
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-09-21T15:10:51Z
dc.identifier.eissn
1863-1711
dc.journal.volume
16
dc.journal.number
4
dc.journal.pagination
1035-1042
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Osorio, Felipe. Universidad Técnica Federico Santa María; Chile
dc.description.fil
Fil: Vallejos, Ronny. Universidad Técnica Federico Santa María; Chile
dc.description.fil
Fil: Barraza, Wilson. U-planner; Chile
dc.description.fil
Fil: Ojeda, Silvia María. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
dc.description.fil
Fil: Landi, Marcos Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; Argentina
dc.journal.title
Signal, Image and Video Processing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11760-021-02051-9
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s11760-021-02051-9
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