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Image Compression Based on Regression Equation

Image Compression Based on Regression Equation. Advisor: H. C. Wu, Y. K. Chan Speaker: Hsin-Nan Tsai ( 蔡信男 ) Date: May 4, 2005. Outline. Introduction The proposed method Experimental results Conclusions. Introduction. YIQ model Quadtree structure Edge detection

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Image Compression Based on Regression Equation

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  1. Image Compression Based on Regression Equation Advisor: H. C. Wu, Y. K. Chan Speaker: Hsin-Nan Tsai (蔡信男) Date: May 4, 2005

  2. Outline • Introduction • The proposed method • Experimental results • Conclusions

  3. Introduction • YIQ model • Quadtree structure • Edge detection • Quadratic regression equation

  4. Y I Q 0.299 0.587 0.114 0.596 -0.275 -0.321 0.212 -0.523 0.311 R G B × = Image compression • RGB YIQ

  5. NW NE SW SE (128x128) (128x128) (128x128) (128x128) (64x64) (64x64) (64x64) (64x64) Image compression (cont.) • Quadtree 1 0 0 1 0 0 0 0 0 Breadth First Traversal Order treelist: 1 0 0 1 0 0 0 0 0

  6. Image compression (cont.) • Edge detection ∆X ∆Y If PCD > DiffTH Count = Count + 1 If Count > CountTH quadtree()

  7. , , and . Image compression (cont.) • Quadratic regression equation The coefficients a0, a1, and a2of this equation can be figured out by following three equations: i is the i-th pixel in an image block, and n is the number of pixels in the image block.

  8. , , and . Image compression (cont.) • Quadratic regression equation The coefficients b0, b1, and b2of this equation can be figured out by following three equations: i is the i-th pixel in an image block, and n is the number of pixels in the image block.

  9. Image compression (cont.) • Compute coefficients colorlist

  10. Ydata Image compression (cont.) • Compress Y values 256 JPEG compression 256 … Y values

  11. Image compression (cont.) Compressed file: treelist || colorlist || Ydata

  12. 1 0 0 1 0 0 0 0 0 treelist: Image decompression • Extract treelist Compressed file: treelist || colorlist || Ydata r is the numbers of 1-bits s is the numbers of 0-bits 3 × r + 1= s

  13. Image decompression (cont.) • Extract colorlist Compressed file: colorlist || Ydata 6 × s

  14. Ydata Image decompression (cont.) • Decompress Ydata 256 JPEG Decompression 256 … Y values

  15. root(256x256) 128x128 128x128 128x128 128x128 64x64 64x64 64x64 64x64 Image decompression (cont.) • Restore quadtree 256 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 256 Y values

  16. Image decompression (cont.) • Substitution coefficients root(256x256) 1 256 0 0 1 0 128x128 128x128 128x128 128x128 256 0 0 0 0 YIQ values 64x64 64x64 64x64 64x64

  17. Image decompression (cont.) • YIQ RGB 256 256 256 256 YIQ values Lena

  18. The PSNRs of the decompressed images in different sizes of regression equation coefficients Experimental results

  19. The PSNRs and CRs of the testing image compressed by JPEG method Experimental results (cont.)

  20. The PSNRs and CRs of the testing image compressed by our method Experimental results (cont.)

  21. Experimental results (cont.) The PSNRs of the testing images encoded by JPEG method in different CRs in different CRs CR Image

  22. Experimental results (cont.) The PSNRs of the testing images encoded by our method in different CRs CR Image

  23. (a) PSNR: 31.503 dB (b) PSNR: 31.542 dB The decompressed images of GIRL4 decoded by our and JPEG methods Experimental results (cont.) • Blocking and Gibbs effects

  24. Conclusions • Comparing to JPEG, the proposed method has good performance with low compression rate

  25. 子宮頸癌細胞抹片影像初始輪廓切割 Speaker: Jun-Dong Chang Advisor: Yung-Kuan Chan,Hsien-Chu Wu Date: 2005/05/04

  26. Introduction • Automatic recognition reduces the carelessness and mistakes caused in artificial recognition. • Initial Contour Segmentation is a pre-process of ACM (Active Contour Model) System. • Initial Contour Segmentation (Background, Cytoplasm, Nucleus)

  27. Color & Texture Analyzing ~ Training Image

  28. Color & Texture Analyzing ~ Training Image (cont.) Background Cytoplasm Nucleus

  29. Regression Function (cont.) Background

  30. Regression Function (cont.) Cytoplasm

  31. Regression Function (cont.) Nucleus

  32. Initial Contour Segmentation arg(min(Dx)) Background Query Image i = arg(min(Dx)), for x = b, c, n.

  33. Initial Contour Segmentation (cont.) Background

  34. Initial Contour Segmentation (cont.) Cytoplasm

  35. Initial Contour Segmentation (cont.) Nucleus

  36. Experimental Results ~ Image 1

  37. Experimental Results~ Image 2

  38. Experimental Results ~ Image 3

  39. Experimental Results ~ Image 4

  40. Conclusions • Most of blocks are segmented at the correct layers. • Blocks of Background Layer are segmented to Cytoplasm Layer. • Regression Function just analyses 2D relation. • We have to correct segmentation errors to improve the accuracy of initial contour segmentation.

  41. Future Work • SVM (Support Vector Machine) • Neighboring Block

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