Detecting blurring artifacts in jpeg2000
compressed images using Classification
Aline Martin
alinemartin@wisc.edu
ECE738 Project – Spring 2005
outline
• Problem statement
• Previous work
• Proposed approach: classification
• Results
• Conclusion and future work
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Problem Statement
Jpeg2000 creates blurring artifacts
Blurred patches in highly textured regions
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Previous work
Rajas A. Sambhare:
“Detecting Artifacts and Textures in Wavelet Coded Images”
ECE 783 Project – Spring 2003
Targets Source
Original image Texture detection Blurring artifacts
detection 4
Previous work
Algorithm:
1 – Detect Textured Regions
2 – Segmentation: k-mean algorithm
3 – Identification of Textured Segments
4 – Identification of segments adjacent to textured Segments
For each Textured Segment
For each adjacent segment
if |mean Source – mean adjacent segment| 6-d Feature vector
- mean Source Segment
- variance Source Segment
- mean Target Segment
- variance Target Segment
- | mean Source Segment - mean Target Segment |
- | mean Source Segment - mean Target Segment |
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Proposed Approach
Classification
2- Training the classifier
Compute m0, m1 : 6 by 1
So, S1 : 6 by 6
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Proposed Approach
Classification
3- Classifier
x: 6-d feature vector computed from a possible Target
P0(x) ~ N(m0,S0)
P1(x) ~ N(m1,S1)
If P1(x) > P0(x) then Class 1
If P0(x) > P1(x) then Class 0
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Results
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Results
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Results
Limitations due to segmentation
Refinement?
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Conclusion and Future work
An algorithm to detect blurring artifacts in jpeg2000
compressed images was developed
Need to improve segmentation: Refinement?
Need a better segmentation algorithm for black and
white images
Need to increase the images database
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