Automatic emulation of human experts for estimating chromatic quality in digitization of graphical documents

Robledano Arillo, Jesús; Moreno Pelayo, Valentín; Pereira Uzal, José Manuel

Publication:
Automatic emulation of human experts for estimating chromatic quality in digitization of graphical documents

dc.affiliation.dpto	UC3M. Departamento de Biblioteconomía y Documentación	es
dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Knowledge Reusing	es
dc.contributor.author	Robledano Arillo, Jesús
dc.contributor.author	Moreno Pelayo, Valentín
dc.contributor.author	Pereira Uzal, José Manuel
dc.date.accessioned	2016-10-06T11:12:50Z
dc.date.available	2016-10-06T11:12:50Z
dc.date.issued	2016
dc.description	Traducción al inglés de una versión ampliada del artículo: Robledano-Arillo, J.; Moreno-Pelayo, V.; Pereira-Uzal, J.M. (2016). Aproximación experimental al uso de métricas objetivas para la estimación de calidad cromática en la digitalización de patrimonio documental gráfico. Revista Española de Documentación Científica, 39 (2): e128. doi: http://dx.doi.org/10.3989/redc.2016.2.1249	es
dc.description.abstract	This work aims to provide a critical examination of different approaches to creating models of automated quality control systems for digital images in digitization projects for photographic heritage collections. It investigates the feasibility of using machine-learning algorithms that work on sets of images previously evaluated by experts to obtain models on which to construct a high performance visual algorithm. We analyzed the data collected after conducting a psychometric experiment in which four human experts evaluated a set of three series of 300 degraded images by assigning each image to different quality classes. This analysis concludes that it is not possible to talk about commonly used simplistic models based on continuous acceptance ranges for colour metrics on an isolated basis, and therefore that it is necessary to investigate more complex models. This study demonstrates that a model based on a machine learning rule-based system employing the CIE 1976 or CIEDE 2000 metrics along with the hue, saturation and lightness colour perceptual attributes emulates the human image quality experts with a high degree of efficacy, above 85%, opening an interesting way to get higher performance visual algorithms to automatically evaluate image quality in the context of digitization of photographic collections.	es
dc.format.extent	33	es
dc.format.mimetype	application/pdf
dc.identifier.uri	https://hdl.handle.net/10016/23693
dc.language.iso	eng	es
dc.relation.isversionof	http://hdl.handle.net/10016/23691
dc.rights	Atribución-NoComercial 3.0 España	*
dc.rights.accessRights	open access	es
dc.rights.uri	http://creativecommons.org/licenses/by-nc/3.0/es/	*
dc.subject.eciencia	Biblioteconomía y Documentación	es
dc.subject.eciencia	Informática	es
dc.subject.other	Document digitization	en
dc.subject.other	Photography	en
dc.subject.other	Image quality assessment	en
dc.subject.other	Machine learning	en
dc.subject.other	C4.5 algorithm	en
dc.subject.other	Visual algorithms	en
dc.subject.other	Digitalización de documentos	es
dc.subject.other	Fotografía	es
dc.subject.other	Evaluación de calidad	es
dc.subject.other	Aprendizaje automático	es
dc.subject.other	Algoritmos visuales	es
dc.title	Automatic emulation of human experts for estimating chromatic quality in digitization of graphical documents	en
dc.type	research article	*
dc.type.hasVersion	SMUR	*
dspace.entity.type	Publication