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A No-Reference Image Blur Metric Based on
theCumulative Probability
of Blur Detection (CPBD)
Niranjan D. Narvekar and Lina J. Karam, Senior Member, IEEE
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Introdution
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This paper deals with no-reference image quality assessment targeted towards blur distortions.
We propose an improved no-reference blur metric which utilizes the concept of just noticeable blur (JNB) together with a cumulative probability of blur detection.
Introdution
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Proposed No-Reference Objective Blur Metric
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= 1 – exp( - ) ………………..(1)
………………..(2)
= ……………….(3)
Proposed No-Reference Objective Blur Metric
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Performance Results
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Performance Results
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LIVE TID2008
Test Sets IVC Toyama
consist of various Gaussian blurred and JPEG2000-compressed images
Performance Results
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………………..(4)
PCC (Pearson correlation coefficient)SROCC (Spearman rank-order correlation)RMSE (root mean squared prediction error)MAE (mean absolute prediction error) OR (outlier ratio)
Performance Results
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Performance Results
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Performance Results
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Conclusion
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It is shown that the proposed metric exhibits consistently a good performance across blur types (Gaussian blur and JPEG2000 blur) and across databases as compared with existing sharpness/blur metrics.
Possible directions of research include extending the metric for assessing blur in videos and 3-D visual content .
Conclusion
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Thanks for your attention !!!