Blind stereo image quality evaluation based on convolutional network and saliency weighting

DOI10.1155/2019/1384921zbMath1435.94055OpenAlexW2972945205WikidataQ127319252 ScholiaQ127319252MaRDI QIDQ2298043

Wujie Zhou

Publication date: 20 February 2020

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2019/1384921

Mathematics Subject Classification ID

Image analysis in multivariate analysis (62H35) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• FSIM

Cites Work

• A perceptual metric for stereoscopic image quality assessment based on the binocular energy
• Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images
• 3D-MAD: A Full Reference Stereoscopic Image Quality Estimator Based on Binocular Lightness and Contrast Perception
• Local and Global Feature Learning for Blind Quality Evaluation of Screen Content and Natural Scene Images
• Waterloo Exploration Database: New Challenges for Image Quality Assessment Models
• FSIM: A Feature Similarity Index for Image Quality Assessment
• No-Reference Quality Assessment of Natural Stereopairs

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