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Figure 10 Sample images from the real blur database.
Then in Section 6.4, the Real blur database (586 Images) is used as a challenging test by which we compare our results with other NR QA blur metrics.
Table 6 Blur QA performance of applying different pooling rules on real blur database Pooling rule SROCC Maximum p% 0.5858 Average 0.5604 Weighted 0.5542.
However, on the Real Blur Database, where the blurs are more complex, possibly nonlinear, and spatially variant, blur perception is more complex and probably more correlated with content (e.g., what is blurred in the image?).
A scatter plot of the scores delivered by our model (following logistic regression) against the MOS scores from the Real Blur Database is shown in Figure 11 showing very good general agreement.
For the Gaussian-blurred database, the proposed NR metric based on image classification performed better than the other NR blur metrics.
For the JPEG database, 14% of the images were classified as images without blocking artifacts, 4% of the JPEG200 database were classified as images with blocking artifacts, and 2% of the Gaussian-blurred database were classified as images with blocking artifacts.
All of the images in the LIVE database are blurred globally.
Figure 11 Plot of predicted objective score versus MOS score of real blur image database.
Performance was demonstrated on the LIVE Image Quality Database and the Real Blur Image Database.
By examining the experimental results from the LIVE Image Quality Database and the Real Blur Image Database, we found that there is a significant performance difference of the models on these two databases.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com