L
Liu Yun
Researcher at Tianjin University
Publications - 18
Citations - 171
Liu Yun is an academic researcher from Tianjin University. The author has contributed to research in topics: Stereo camera & Image quality. The author has an hindex of 6, co-authored 18 publications receiving 162 citations. Previous affiliations of Liu Yun include Loughborough University & University of California, Berkeley.
Papers
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Stereoscopic image quality assessment method based on binocular combination saliency model
TL;DR: An effective method to evaluate the quality of stereoscopic images that are afflicted by symmetric distortions is proposed and a new 3D saliency map is developed, which not only greatly reduces the computational complexity by avoiding calculation of the depth information, but also assigns appropriate weights to the image contents.
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A perceptual stereoscopic image quality assessment model accounting for binocular combination behavior
TL;DR: Experimental results demonstrate that the proposed quality assessment metric significantly outperforms the existing metrics and can achieve higher consistency with subject quality assessment when predicting the quality of stereoscopic images that have been symmetrically distorted.
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Objective Evaluation Criteria for Stereo Camera Shooting Quality Under Different Shooting Parameters and Shooting Distances
TL;DR: This work proposes the objective evaluation criteria of shooting quality of two types of stereo cameras (parallel and toed-in camera configurations) under three shooting conditions (macro shooting, short, and long distance shooting).
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New stereo shooting evaluation metric based on stereoscopic distortion and subjective perception
TL;DR: New shooting formulae for both toed-in and parallel camera configurations are presented and a new shooting evaluation metric is proposed for the evaluation of stereo camera quality.
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Research on medical applications of contrast sensitivity function to red–green gratings in 3D space
TL;DR: Simulation results demonstrate that the proposed model of red–green contrast sensitivity in 3D space is well fitted and achieves high consistency with human chromatic visual characteristics.