Learning Local Quality-Aware Structures of Salient Regions for Stereoscopic Images via Deep Neural Networks

The perceptual quality of stereoscopic images plays an essential role in the human perception of visual information. However, most available stereoscopic image quality assessment (SIQA) methods evaluate 3D visual experience using hand-crafted features or shallow architectures, which cannot model the...

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Veröffentlicht in:	IEEE transactions on multimedia 2020-11, Vol.22 (11), p.2938-2949
Hauptverfasser:	Sun, Guangming, Shi, Bufan, Chen, Xiaodong, Krylov, Andrey S., Ding, Yong
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Convolutional neural network Feature extraction Image quality Machine learning Neural networks Quality assessment Stereo image processing stereoscopic image quality assessment Stereoscopy Task analysis three-column deep model Three-dimensional displays Training data visual saliency Visualization
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