Parts-based stereoscopic image assessment by learning binocular manifold color visual properties-Reference-Cited by-同舟云学术

Parts-based stereoscopic image assessment by learning binocular manifold color visual properties

Published:2016-10-26 Issue:6 Volume:25 Page:061611
ISSN:1017-9909
Container-title:Journal of Electronic Imaging
language:en
Short-container-title:J. Electron. Imaging

Author:

Xu Haiyong¹,Yu Mei²,Luo Ting¹,Zhang Yun³,Jiang Gangyi²

Affiliation:

1. Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, ChinabNingbo University, College of Science and Technology, Ningbo 315212, China

2. Ningbo University, Faculty of Information Science and Engineering, Ningbo 315211, China

3. Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China

Publisher

SPIE-Intl Soc Optical Eng

Subject

Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics

Reference32 articles.

1. Stereoscopic image quality assessment using disparity-compensated view filtering

2. Blind Image Quality Assessment for Stereoscopic Images Using Binocular Guided Quality Lookup and Visual Codebook

3. Image Quality Assessment: From Error Visibility to Structural Similarity

4. Image information and visual quality

5. FSIM: A Feature Similarity Index for Image Quality Assessment

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1. Stereoscopic Image Quality Assessment Based on Binocular Combination;Advanced Topics in Science and Technology in China;2020

2. Toward a Quality Predictor for Stereoscopic Images via Analysis of Human Binocular Visual Perception;IEEE Access;2019

3. Stereoscopic Image Quality Assessment by Considering Binocular Visual Mechanisms;IEEE Access;2018

4. Full-reference quality assessment of stereoscopic images by learning binocular visual properties;Applied Optics;2017-10-10