3D No-Reference Image Quality Assessment via Transfer Learning and Saliency-Guided Feature Consolidation

Motivated by the success of convolutional neural networks (CNNs) in image-related applications, in this paper, we design an effective method for no-reference 3D image quality assessment (3D IQA) through CNN-based feature extraction and consolidation strategy. In the first and most vital stage, quali...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access 2019, Vol.7, p.85286-85297
Hauptverfasser:	Xu, Xiaogang, Shi, Bufan, Gu, Zijin, Deng, Ruizhe, Chen, Xiaodong, Krylov, Andrey S., Ding, Yong
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Consolidation deep neural network Distortion Feature extraction Image quality Machine learning No-reference 3D image quality assessment Quality assessment Salience Statistical analysis Support vector machines Three-dimensional displays Training data transfer learning Two dimensional displays
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!