Rotation algorithm artifacts in stereoscopic images

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

Vertical Retargeting for Stereoscopic Images via Stereo Seam Carving

2020 ◽  
Vol 16 (4) ◽  
pp. 1-22
Author(s):  
Kun Zeng ◽  
Jiangchuan Hu ◽  
Yongyi Gong ◽  
Kanoksak Wattanachote ◽  
Runpeng Yu ◽  
...  
Keyword(s):  
Seam Carving ◽  
Stereoscopic Images

scholarly journals Constructing Stereo Presentations of Textual Information on an Autostereoscopic Monitor

2019 ◽  
Author(s):  
Сергей Андреев ◽  
Sergey Andreev ◽  
Николь Бондарева ◽  
Nicole Bondareva
Keyword(s):  
Numerical Simulation ◽  
Scientific Research ◽  
Practical Experience ◽  
Stereo Image ◽  
Stereo Images ◽  
General Study ◽  
Textual Information ◽  
Stereoscopic Images ◽  
Depth Maps

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.

scholarly journals No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Peng Xu ◽  
Man Guo ◽  
Lei Chen ◽  
Weifeng Hu ◽  
Qingshan Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Statistical Features ◽  
Statistical Regression ◽  
Stereoscopic Image ◽  
Machine Learning Technique ◽  
Stereoscopic Images ◽  
Structure Representation ◽  
Learning Technique

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.

Stereoscopic images disparity for predictive coding

2003 ◽  
Author(s):  
H. Yamaguchi ◽  
Y. Tatehira ◽  
K. Akiyama ◽  
Y. Kobayashi
Keyword(s):  
Predictive Coding ◽  
Stereoscopic Images
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