TY - GEN
T1 - Reduced-reference quality assessment based on the entropy of DNT coefficients of locally weighted gradients
AU - Golestaneh, S. Alireza
AU - Karam, Lina
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/9
Y1 - 2015/12/9
N2 - Reduced-reference (RR) image quality assessment (IQA) methods make use of partial information or features extracted from the reference image for estimating the quality of distorted images. Finding a balance between the number of RR features and image quality estimation accuracy is a difficult task. This paper presents a training-free low cost RRIQA method which requires a very small number of RR features (6 RR features). The proposed RRIQA algorithm is based on the divisive normalization transform (DNT) of locally weighted gradient magnitudes. The weighting of the gradient magnitudes is performed in a locally adaptive manner based on the human visual system's contrast sensitivity and neighborhood gradient information. The RR features are obtained by computing the entropy of each DNT subband and, for each scale, averaging the subband entropies along all orientations, resulting in L RR features (one average entropy per scale) for an L-level DNT. Performance evaluations on four large-scale benchmark databases demonstrates that the proposed RRIQA method delivers highly competitive performance as compared to the state-of-the-art RRIQA models as well as full reference ones.
AB - Reduced-reference (RR) image quality assessment (IQA) methods make use of partial information or features extracted from the reference image for estimating the quality of distorted images. Finding a balance between the number of RR features and image quality estimation accuracy is a difficult task. This paper presents a training-free low cost RRIQA method which requires a very small number of RR features (6 RR features). The proposed RRIQA algorithm is based on the divisive normalization transform (DNT) of locally weighted gradient magnitudes. The weighting of the gradient magnitudes is performed in a locally adaptive manner based on the human visual system's contrast sensitivity and neighborhood gradient information. The RR features are obtained by computing the entropy of each DNT subband and, for each scale, averaging the subband entropies along all orientations, resulting in L RR features (one average entropy per scale) for an L-level DNT. Performance evaluations on four large-scale benchmark databases demonstrates that the proposed RRIQA method delivers highly competitive performance as compared to the state-of-the-art RRIQA models as well as full reference ones.
KW - Gaussian filtering
KW - Reduced reference quality assessment (RRIQA)
KW - contrast sensitivity function (CSF)
KW - divisive normalization transform
KW - entropy
UR - http://www.scopus.com/inward/record.url?scp=84956624573&partnerID=8YFLogxK
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U2 - 10.1109/ICIP.2015.7351580
DO - 10.1109/ICIP.2015.7351580
M3 - Conference contribution
AN - SCOPUS:84956624573
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 4117
EP - 4120
BT - 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PB - IEEE Computer Society
T2 - IEEE International Conference on Image Processing, ICIP 2015
Y2 - 27 September 2015 through 30 September 2015
ER -