Toward a Blind Deep Quality Evaluator for Stereoscopic Images Based on Monocular and Binocular Interactions

2016 ◽  
Vol 25 (5) ◽  
pp. 2059-2074 ◽  
Author(s):  
Feng Shao ◽  
Weijun Tian ◽  
Weisi Lin ◽  
Gangyi Jiang ◽  
Qionghai Dai
Author(s):  
Kun Zeng ◽  
Jiangchuan Hu ◽  
Yongyi Gong ◽  
Kanoksak Wattanachote ◽  
Runpeng Yu ◽  
...  

1992 ◽  
Vol 55 ◽  
pp. 114
Author(s):  
B. Bagolini ◽  
V. Porciatti ◽  
B. Falsini ◽  
K. Dickmann ◽  
G. Porrello ◽  
...  

1990 ◽  
Vol 29 (8) ◽  
pp. 973 ◽  
Author(s):  
Larry F. Hodges
Keyword(s):  

Author(s):  
Сергей Андреев ◽  
Sergey Andreev ◽  
Николь Бондарева ◽  
Nicole Bondareva

This paper presents practical experience in constructing stereo presentations of texts and formulas on an autostereoscopic monitor in stereo presentations designed to display the results of numerical simulation. The task of constructing stereo images of texts and formulas is a structural subtask of a general study devoted to the development of methods and algorithms for constructing stereo presentations of the results of scientific research. This paper discusses the construction of stereoscopic images on an autostereoscopic monitor. The autostereoscopic monitor allows one to observe a stereo image without glasses, while ensuring the quality of the stereo image, which is not inferior to the quality of the stereo image, presented using a classic 3D projection stereo system. Various methods of obtaining stereo images supported by the monitor were tested, namely, the multi-view presentation of the object and the construction of depth maps. The results for both methods are presented.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Peng Xu ◽  
Man Guo ◽  
Lei Chen ◽  
Weifeng Hu ◽  
Qingshan Chen ◽  
...  

Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.


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