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Motivation
• Compare performance of different image metrics for JPEG images with subjective measurement– Blocking is the dominant artifact in JPEG images (or other block-
based coding), especially at low-bit-rate
– Post-processing may incur blurring when reducing blocking
– Need a good metrics
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Candidate Metrics
• RMSE (root-mean-square error)
• BMR (block-to-mask ratio, Liu 1997)
• EOBD (effect-of-block-distortion, Eskicioglu 1995)
• MIX (RMSE + BMR)– RMSE is pixel-based, and BMR is block-based,
combination may be more robust
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BMR: I• Compute the block difference
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jiLjiLjiLjiLjiL bottomtoprightleft
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Block Border
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BMR: II
• Include the perceptual effects
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),(log50),(
jiL
jiLjiBMR
JND
),( jiLJNDwhere is the just-noticeable difference
50 is a weighted ratio
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BMR: III
• Separate the blocking and blurring measure• OBMR(i,j): BMR in the original image
• PBMR(i,j): BMR in the processed image.
– a) PBMR(i,j) > OBMR(i,j). Block(i,j) in processed image is more blocking than that of the original image.
– b) PBMR(i,j) <= OBMR(i,j). Block(i,j) is blurred in processed image.
– blocking strength = mean(|OBMR(i,j)-PBMR(i,j)|) for set a– blurring strength = mean(|OBMR(i,j)-PBMR(i,j)|) for set b
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ExperimentsClick on the image with the worst quality
JPEG JPEG withFiltering (3x3)
JPEG withde-block
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Experiments (cont.)
• Each experiment has18x3 images:– 18 JPEG images at quality levels 5~40
(bits .25~.80 bpp)– 18 smoothed (3x3) JPEG images– 18 de-blocked JPEG images (Chou’s 1995)
• Repeat 4 times
• 2 subjects, 2 image sets (‘lena’ & ‘einstein’)
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Results: ComparisonM
ean
Ran
k E
rror
RMSE BMR MIX EOBD
Rank Error for Image i:Ei= | Si – Ri |, where Si is the subjective rank of image I, Ri is the rank derived from metrics