scholarly journals Reduced-reference image quality metric based on statistic model in complex wavelet transform domain

2019 ◽  
Vol 74 ◽  
pp. 218-230
Author(s):  
Xinwen Xie ◽  
Philippe Carré ◽  
Clency Perrine ◽  
Yannis Pousset ◽  
Nanrun Zhou ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Reference Image ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
Quality Metric ◽  
Statistic Model ◽  
Image Quality Metric ◽  
Complex Wavelet

How re-training process affect the performance of no-reference image quality metric for face images

2019 ◽  
Vol 2019 (5) ◽  
pp. 528-1-528-6
Author(s):  
Xinwei Liu ◽  
Christophe Charrier ◽  
Marius Pedersen ◽  
Patrick Bours
Keyword(s):  
Image Quality ◽  
Reference Image ◽  
Training Process ◽  
Quality Metric ◽  
Face Images ◽  
Image Quality Metric

scholarly journals NoRM: No-Reference Image Quality Metric for Realistic Image Synthesis

2012 ◽  
Vol 31 (2pt3) ◽  
pp. 545-554 ◽  
Author(s):  
Robert Herzog ◽  
Martin Čadík ◽  
Tunç O. Aydčin ◽  
Kwang In Kim ◽  
Karol Myszkowski ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Synthesis ◽  
Reference Image ◽  
Quality Metric ◽  
Image Quality Metric ◽  
Realistic Image

Compound Fault Feature Separation of Rolling Bearing Based on Complex Wavelet and Energy Operator Demodulation

2012 ◽  
Vol 226-228 ◽  
pp. 765-771
Author(s):  
Yang Yang ◽  
Jian Yu Zhang ◽  
Sui Zheng Zhang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Coefficient ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Difficult Problem ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
Transform Coefficients ◽  
Complex Wavelet ◽  
Fault Information

Compound fault feature separation is a difficult problem in diagnosis field of mechanical system. For the rolling bearing with compound fault on outer and inner race, feature separation technology based on complex wavelet transform and energy operator demodulation is introduced. Through continuous wavelet transform, coefficients of mixed fault signal can be achieved in different wavelet transform domain (i.e. real, imaginary, modulus and phase domain). Furthermore, wavelet power spectrum contours and time average wavelet energy spectrum are applied to extract the scales which hold rich fault information, and the wavelet coefficient slice of specific scale is also drawn. For wavelet coefficients in different domain, spectrum analysis and energy operator demodulation can be used successfully to separate mixed fault. The comparison of feature extraction effect between complex wavelet and real wavelet transform shows that complex wavelet transform is obviously better than the latter.

Sign in / Sign up

Export Citation Format

Share Document