a comparison of quality metrics for jpeg images
DESCRIPTION
A Comparison of Quality Metrics for JPEG Images. Feng Xiao Fall 2000. 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 - PowerPoint PPT PresentationTRANSCRIPT
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A Comparison of Quality Metrics for JPEG Images
Feng Xiao
Fall 2000
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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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Block Border
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BMR: II
• Include the perceptual effects
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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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BMR: IV
• Construct the single BMR
BMR= blocking strength + blurring strength
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BMR: V
JPEG quality
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engt
h
Str
engt
h
Size of smoothing filter
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EOBD
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NmfEnMfEEOBD
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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
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Results: Post-processing
Bit Rate (bpp)
Impr
ovem
ent (
rank
ord
er)
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Results: RMSE vs. Subjective
Subjective Rank Order
RMSE
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Results: BMR vs. Subjective
Subjective Rank Order
BMR
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Results: EOBD vs. Subjective
EOBD
Subjective Rank Order
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Results: MIX vs. Subjective
MIX
Subjective Rank Order
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Conclusion
• MIX is the best metrics as tested– It takes both pixel-based metrics (RMSE) and block-based metrics
(BMR) into consideration.
• Both smooth (3x3) and de-block (chou’s) show improvement for low bit-rate.