Blind image quality assessment for Gaussian blur images using exact Zernike moments and gradient magnitude
DOI10.1016/j.jfranklin.2016.08.012zbMath1349.94024OpenAlexW2517995592MaRDI QIDQ344664
Raveendran Paramesran, Chern-Loon Lim, King Ngi Ngan, Wissam A. Jassim, Yong-Poh Yu
Publication date: 24 November 2016
Published in: Journal of the Franklin Institute (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jfranklin.2016.08.012
blur image quality metricblurring on digital imagesexact Zernike moments (EZMs)gradient magnitude (GM)support vector machine regressor (SVR)
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
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- Content-based image quality metric using similarity measure of moment vectors
- Image quality assessment by discrete orthogonal moments
- On image analysis by the methods of moments
- Image analysis by Tchebichef moments
- Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
- Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features
- A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)
- Combined Invariants to Similarity Transformation and to Blur Using Orthogonal Zernike Moments
- Hybrid No-Reference Natural Image Quality Assessment of Noisy, Blurry, JPEG2000, and JPEG Images
- FSIM: A Feature Similarity Index for Image Quality Assessment
- A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD)
- 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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