No-Reference Image Quality Evaluation Model for JPEG and JPEG2000 Images

2008 ◽  
Vol 3 (3) ◽  
pp. 352-359 ◽  
Author(s):  
Z. M. Parvez Sazzad ◽  
Y. Kawayoke ◽  
Y. Horita
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Reference Image ◽  
Image Quality Evaluation ◽  
Quality Evaluation Model

Image Quality Evaluation Model Based on Local Features and Segmentation

2006 ◽  
Author(s):  
Yuukou Horita ◽  
Masaharu Sato ◽  
Yoshikazu Kawayoke ◽  
Z. M. Parvez Sazzad ◽  
Keiji Shibata
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Local Features ◽  
Image Quality Evaluation ◽  
Model Based ◽  
Quality Evaluation Model

3D Image Quality Evaluation Model in Stereoscopic Imaging Technology

2013 ◽  
Vol 19 (2) ◽  
pp. 449-454
Author(s):  
Wan-Young Chung ◽  
Rohit Ramesh ◽  
Shin-ll Jeong ◽  
Gi-Sik Byun
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
3D Image ◽  
Imaging Technology ◽  
Image Quality Evaluation ◽  
Stereoscopic Imaging ◽  
Quality Evaluation Model

MUNIQUE: Multiview no-reference image quality evaluation

2015 ◽  
Author(s):  
Jose Vinicius de Miranda Cardoso ◽  
Carlos Danilo Miranda Regis ◽  
Marcelo Sampaio de Alencar
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Reference Image ◽  
Image Quality Evaluation

Study on the Evaluation Model of Image Definition

2010 ◽  
Vol 20-23 ◽  
pp. 636-640 ◽  
Author(s):  
Guo Jin Chen ◽  
Miao Fen Zhu ◽  
Wan Qiang Wang ◽  
Yong Ning Li
Keyword(s):  
Image Quality ◽  
Real Time ◽  
Autocorrelation Function ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Change Rate ◽  
Image Quality Evaluation ◽  
Factors Affecting ◽  
Evaluation Models ◽  
Image Definition

At present, the usual image definition evaluation methods are poor in focusing precision and have a lot of calculations. In view of these problems, the paper analyzed the factors affecting an image quality evaluation, and proposed two evaluation models based on an image contrast change rate and an autocorrelation function. These methods avoided efficiently the phenomenon of partial peaks of focusing-function curves for noise disturbing. The stimulant test proves that the focusing curve of the evaluation model in the paper has many advantages, such as good focusing performance, strong antijamming ability, good real time and so on.

Infrared image quality evaluation method without reference image

2013 ◽  
Author(s):  
Song Yue ◽  
Tingting Ren ◽  
Chengsheng Wang ◽  
Bo Lei ◽  
Zhijie Zhang
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Infrared Image ◽  
Reference Image ◽  
Image Quality Evaluation
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