Abstract
The target of no-reference (NR) image quality assessment (IQA) is to establish a computational model to predict the visual quality of an image. The existing prominent method is based on natural scene statistics (NSS). It uses the joint and marginal distributions of wavelet coefficients for IQA. However, this method is only applicable to JPEG2000 compressed images. Since the wavelet transform fails to capture the directional information of images, an improved NSS model is established by contourlets. In this paper, the contourlet transform is utilized to NSS of images, and then the relationship of contourlet coefficients is represented by the joint distribution. The statistics of contourlet coefficients are applicable to indicate variation of image quality. In addition, an image-dependent threshold is adopted to reduce the effect of content to the statistical model. Finally, image quality can be evaluated by combining the extracted features in each subband nonlinearly. Our algorithm is trained and tested on the LIVE database II. Experimental results demonstrate that the proposed algorithm is superior to the conventional NSS model and can be applied to different distortions.
| Original language | English |
|---|---|
| Pages (from-to) | 784-794 |
| Number of pages | 11 |
| Journal | Neurocomputing |
| Volume | 73 |
| Issue number | 4-6 |
| Early online date | 20 Nov 2009 |
| DOIs | |
| Publication status | Published - Jan 2010 |
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Bibliographical note
Bayesian Networks / Design and Application of Neural Networks and Intelligent Learning Systems (KES 2008 / Bio-inspired Computing: Theories and Applications (BIC-TA 2007)Keywords
- Contourlets
- Image modeling
- Image quality assessment
- No reference
Cite this
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No-reference image quality assessment in contourlet domain. / Lu, Wen; Zeng, Kai; Tao, Dacheng; Yuan, Yuan; Gao, Xinbo.
In: Neurocomputing, Vol. 73, No. 4-6, 01.2010, p. 784-794.Research output: Contribution to journal › Article
TY - JOUR
T1 - No-reference image quality assessment in contourlet domain
AU - Lu, Wen
AU - Zeng, Kai
AU - Tao, Dacheng
AU - Yuan, Yuan
AU - Gao, Xinbo
N1 - Bayesian Networks / Design and Application of Neural Networks and Intelligent Learning Systems (KES 2008 / Bio-inspired Computing: Theories and Applications (BIC-TA 2007)
PY - 2010/1
Y1 - 2010/1
N2 - The target of no-reference (NR) image quality assessment (IQA) is to establish a computational model to predict the visual quality of an image. The existing prominent method is based on natural scene statistics (NSS). It uses the joint and marginal distributions of wavelet coefficients for IQA. However, this method is only applicable to JPEG2000 compressed images. Since the wavelet transform fails to capture the directional information of images, an improved NSS model is established by contourlets. In this paper, the contourlet transform is utilized to NSS of images, and then the relationship of contourlet coefficients is represented by the joint distribution. The statistics of contourlet coefficients are applicable to indicate variation of image quality. In addition, an image-dependent threshold is adopted to reduce the effect of content to the statistical model. Finally, image quality can be evaluated by combining the extracted features in each subband nonlinearly. Our algorithm is trained and tested on the LIVE database II. Experimental results demonstrate that the proposed algorithm is superior to the conventional NSS model and can be applied to different distortions.
AB - The target of no-reference (NR) image quality assessment (IQA) is to establish a computational model to predict the visual quality of an image. The existing prominent method is based on natural scene statistics (NSS). It uses the joint and marginal distributions of wavelet coefficients for IQA. However, this method is only applicable to JPEG2000 compressed images. Since the wavelet transform fails to capture the directional information of images, an improved NSS model is established by contourlets. In this paper, the contourlet transform is utilized to NSS of images, and then the relationship of contourlet coefficients is represented by the joint distribution. The statistics of contourlet coefficients are applicable to indicate variation of image quality. In addition, an image-dependent threshold is adopted to reduce the effect of content to the statistical model. Finally, image quality can be evaluated by combining the extracted features in each subband nonlinearly. Our algorithm is trained and tested on the LIVE database II. Experimental results demonstrate that the proposed algorithm is superior to the conventional NSS model and can be applied to different distortions.
KW - Contourlets
KW - Image modeling
KW - Image quality assessment
KW - No reference
UR - http://www.scopus.com/inward/record.url?scp=75749156539&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2009.10.012
DO - 10.1016/j.neucom.2009.10.012
M3 - Article
AN - SCOPUS:75749156539
VL - 73
SP - 784
EP - 794
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
IS - 4-6
ER -