1. Aline Martin alinemartin@wisc.edu ECE738 Project – Spring 2005
Detecting blurring artifacts in jpeg2000 compressed images using Classification
Aline Martin
ECE738 Project – Spring 2005

2. • Proposed approach: classification • Results
outline
• Problem statement
• Previous work
• Proposed approach: classification
• Results
• Conclusion and future work

3. Problem Statement Jpeg2000 creates blurring artifacts
Blurred patches in highly textured regions

4. 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

5. 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| < 0.2
then target segment
end

6. Previous work
Drawbacks: Heuristic Threshold
Does not perform well on other images
Proposed Approach: Use Classification to automatically classify smooth regions as blurring artifacts or smooth regions

7. Proposed Approach
Class0: Source is a texture and Target is not a blurring artifact
Class1: Source is a texture and Target is a blurring artifact
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
compute feature vector
classify it as Class0 or Class1
end

8. Proposed Approach Original Image Identification of Textured areas
Segmentation
Segments edges
Textured Segments

9. Proposed Approach Classification
1- Features extraction -> 6-d Feature vector
- mean Source Segment
- variance Source Segment
- mean Target Segment
- variance Target Segment
- | mean Source Segment - mean Target Segment |

10. Proposed Approach Classification 2- Training the classifier
Compute m0, m1 : 6 by 1
So, S1 : 6 by 6

11. 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

12. Results

13. Results

14. Results
Limitations due to segmentation
Refinement?

15. 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