Objective Prediction of Human Visual Acuity Using Image Quality Metrics
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http://hdl.handle.net/10045/134689
Título: | Objective Prediction of Human Visual Acuity Using Image Quality Metrics |
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Autor/es: | Espinosa, Julián | Pérez Rodríguez, Jorge | Mas, David | Vázquez Ferri, Carmen | Perales, Esther |
Grupo/s de investigación o GITE: | Grupo de Análisis de Imagen, Sistemas Ópticos y Visión (IMAOS+V) | Visión y Color |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Óptica, Farmacología y Anatomía |
Palabras clave: | Visual acuity | Aberration | Image quality assessment |
Fecha de publicación: | 22-may-2023 |
Editor: | MDPI |
Cita bibliográfica: | Tomás JE, Rodríguez JP, Candela DM, Ferri CV, Perales E. Objective Prediction of Human Visual Acuity Using Image Quality Metrics. Applied Sciences. 2023; 13(10):6350. https://doi.org/10.3390/app13106350 |
Resumen: | This work addresses the objective prediction of human uncorrected decimal visual acuity, an unsolved challenge due to the contribution of both physical and neural factors. An alternative approach to assess the image quality of the human visual system can be addressed from the image and video processing perspective. Human tolerance to image degradation is quantified by mean opinion scores, and several image quality assessment algorithms are used to maintain, control, and improve the quality of processed images. The aberration map of the eye is used to obtain the degraded theoretical image from a set of natural images. The amount of distortion added by the eye to the natural image was quantified using different image processing metrics, and the correlation between the result of each metric and subjective visual acuity was assessed. The correlation obtained for a model based on a linear combination of the normalized mean square error metric and the feature similarity index metric was very good. It was concluded that the proposed method could be an objective way to determine subjects’ monocular and uncorrected decimal visual acuity with low uncertainty. |
URI: | http://hdl.handle.net/10045/134689 |
ISSN: | 2076-3417 |
DOI: | 10.3390/app13106350 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/app13106350 |
Aparece en las colecciones: | INV - IMAOS+V - Artículos de Revistas INV - GVC - Artículos de Revistas |
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Espinosa-Tomas_etal_2023_ApplSci.pdf | 2,08 MB | Adobe PDF | Abrir Vista previa | |
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