A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information.

Shen, Liquan; Yao, Yang; Geng, Xianqiu; Fang, Ruigang; Wu, Dapeng

Shen, Liquan; Yao, Yang; Geng, Xianqiu; Fang, Ruigang; Wu, Dapeng.

Afiliación

Shen L; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China.
Yao Y; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China.
Geng X; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China.
Fang R; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32603, USA.
Wu D; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32603, USA.

Sensors (Basel) ; 23(13)2023 Jul 07.

Article en En | MEDLINE | ID: mdl-37448078

Asunto(s)

Percepción de Profundidad; Imagenología Tridimensional; Imagenología Tridimensional/métodos; Percepción Visual; Atención; Bases de Datos Factuales

Palabras clave

machine learning; natural scene statistics; no reference; spatial domain; stereo visual information; stereoscopic image quality assessment; transform domain

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Imagenología Tridimensional / Percepción de Profundidad Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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