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

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

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

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

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

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

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

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

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 |

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

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

Results

Results

Results Limitations due to segmentation Refinement?

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