Learning a referenceless stereopair quality engine with deep nonnegativity constrained sparse autoencoder

•A three-column deep non-negativity constrained sparse autoencoder is proposed for BSIQA.•Both feature evolution and feature mapping are addressed in a unified framework for BSIQA.•A Bayesian inference-based quality combination framework is used to derive 3D quality score. This paper proposes a no-r...

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Veröffentlicht in:	Pattern recognition Jg. 76; S. 242 - 255
Hauptverfasser:	Jiang, Qiuping, Shao, Feng, Lin, Weisi, Jiang, Gangyi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.04.2018
Schlagworte:	Deep learning Image quality assessment No-reference/referenceless Nonnegativity constrained Sparse autoencoder Stereoscopic 3D image Stereoscopic 3D image Deep learning Image quality assessment No-reference/referenceless Sparse autoencoder Nonnegativity constrained
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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