1. 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. 2 Outline Introduction The proposed method Experimental results Conclusions

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

4. 4 Image compression RGB YIQ YIQYIQ 0.299 0.587 0.114 0.596 -0.275 -0.321 0.212 -0.523 0.311 RGBRGB = ×

5. 5 Image compression (cont.) Quadtree (128x128) (64x64) NWNESW SE 1 0010 0000 Breadth First Traversal Order treelist: 1 0 0 1 0 0 0 0 0

6. 6 Image compression (cont.) Edge detection 129192188191 123192188185 122178180183 126173175 ∆X ∆Y If PCD > DiffTH Count = Count + 1 If Count > CountTH quadtree()

7. 7 Image compression (cont.) Quadratic regression equation The coefficients a 0, a 1, and a 2 of this equation can be figured out by following three equations:.,, and i is the i-th pixel in an image block, and n is the number of pixels in the image block.

8. 8 Image compression (cont.) Quadratic regression equation The coefficients b 0, b 1, and b 2 of this equation can be figured out by following three equations:.,, and i is the i-th pixel in an image block, and n is the number of pixels in the image block.

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

10. 10 Image compression (cont.) Compress Y values 100251…325 12… 256 Y values JPEG compression Ydata …

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

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

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

14. 14 Image decompression (cont.) Decompress Ydata Ydata JPEG Decompression 101253…625 12… 256 Y values …

15. 15 Image decompression (cont.) Restore quadtree Y values 256 1 0 0100000 root(256x256) 128x128 64x64 1 0 0 1 0 0 0 0 0

16. 16 Image decompression (cont.) Substitution coefficients root(256x256) 128x128 64x64 1 1000 0000 YIQ values 256

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

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

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

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

21. 21 Experimental results (cont.) 345678910111213 F1637.0736.5936.1235.6735.2334.8134.4034.0033.6233.2532.89 GIRL536.1235.8335.5535.2735.0034.7434.4934.2434.0033.7633.53 HOUSE34.3834.1733.9633.7633.5633.3733.1833.0032.8232.6432.47 SAILBOAT33.4432.9132.4031.9131.4430.9930.5630.1529.7529.3729.01 SPLASH36.9436.6336.3336.0435.7635.4835.2234.9534.7034.4534.21 The PSNRs of the testing images encoded by JPEG method in different CRs in different CRs CR Image

22. 22 Experimental results (cont.) 345678910111213 F1639.1338.3237.5236.7235.9134.6533.4332.1730.8930.1128.31 GIRL539.0438.2337.4136.6035.8235.0834.2233.3032.1730.3827.98 HOUSE37.5037.1236.7336.3535.9735.5935.1234.6434.0733.4132.80 SAILBOAT34.7134.0033.2932.4831.5230.5329.7329.0428.1527.1325.95 SPLASH40.1839.5838.9938.3937.8037.1936.5535.7434.8433.6031.84 The PSNRs of the testing images encoded by our method in different CRs CR Image

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

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

25. 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. 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, Nucl eus)

27. 27 Color & Texture Analyzing ~ Training Image

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

29. 29 Regression Function (cont.) Background

30. 30 Regression Function (cont.) Cytoplasm

31. 31 Regression Function (cont.) Nucleus

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

33. 33 Initial Contour Segmentation (cont.) Background

34. 34 Initial Contour Segmentation (cont.) Cytoplasm

35. 35 Initial Contour Segmentation (cont.) Nucleus

36. 36 Experimental Results ~ Image 1

37. 37 Experimental Results ~ Image 2

38. 38 Experimental Results ~ Image 3

39. 39 Experimental Results ~ Image 4

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

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