A hybrid learning-based framework for blind image quality assessment

DOI10.1007/S11045-017-0475-YzbMath1448.94038OpenAlexW2588987112MaRDI QIDQ784585

Li Chen, Meiyin Wu, Jing Tian

Publication date: 3 August 2020

Published in: Multidimensional Systems and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11045-017-0475-y

zbMATH Keywords

feature extractionsupport vector regressionconvolutional neural networkblind video image quality

Mathematics Subject Classification ID

Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• LIBSVM
• TID2013
• MatConvNet
• FSIM
• CID2013

Cites Work

• Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
• CID2013: A Database for Evaluating No-Reference Image Quality Assessment Algorithms
• A Feature-Enriched Completely Blind Image Quality Evaluator
• No-Reference Image Sharpness Assessment in Autoregressive Parameter Space
• FSIM: A Feature Similarity Index for Image Quality Assessment
• Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
• No-Reference Image Quality Assessment in the Spatial Domain

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