No-reference stereoscopic image quality assessment on both complex contourlet and spatial domain via Kernel ELM

Stereoscopic imaging is widely used in many fields. To guarantee the best quality of experience, it is necessary to design a robust and accurate quality assessment model for stereoscopic content. In this paper, we proposed a no-reference stereoscopic image quality assessment (NR-SIQA) model using bo...

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Veröffentlicht in:	Signal processing. Image communication 2022-02, Vol.101, p.116547, Article 116547
Hauptverfasser:	Guan, Tuxin, Li, Chaofeng, Zheng, Yuhui, Zhao, Shenghu, Wu, Xiaojun
Format:	Artikel
Sprache:	eng
Schlagworte:	CIELAB color space Complex contourlet transform Domains Feature extraction Image quality Kernel extreme learning machine Kernels No reference stereoscopic image quality assessment Quality assessment Regression models Statistics Stereoscopy Visual discomfort
Online-Zugang:	Volltext
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