2010 년 04 월 28 일 백준기 중앙대학교 첨단영상대학원 시각및지능시스템연구실

1. (156-756) 서울시 동작구 흑석동 221 중앙대학교 첨단영상대학원, 시각및지능시스템연구실 (02) 820-5300, 010-7123-6846 http://ipis.cau.ac.kr; email@cau.email CH06: Color Image Processing 2010년 04월 28일 백준기 중앙대학교 첨단영상대학원 시각및지능시스템연구실

2. Chapter 6. Color Image Processing Joonki Paik Graduate School of Advanced Imaging Science, Multimedia, and Film Chung-Ang University

3. Table of Contents • Color fundamentals • Color models • Pseudocolor image processing • Basics of full-color image processing • Color transformations • Smoothing and sharpening • Image segmentation based on color • Noise in color images • Color image compression

4. 6.1 Color Fundamentals • Isaac Newton (1666) • The electromagnetic spectrum

5. Achromatic: void of color • Only intensity or amount, gray level, black-and-white, single channel, etc. • Three quantities describing the chromatic light source • Radiance [W]: total amount of energy from the light source • Luminance [lm]: amount of energy an observer perceives • Brightness: amount of brightness subjectively recognized by an observer (the human visual system)

6. Color sensors: cones in the retina (6~7 millions) • Three primary color sensing cones: Red (65%), Green (33%), blue (2%, most sensitive) • Secondary colors: magenta (R+B), cyan (G+B), yellow (R+G) • Additive primaries: R+G+B=W • Subtractive primaries: R+G+B=B

7. A color distinction characteristics • Brightness, hue, and saturation • Chromaticity: determined by hue and saturation • Tristimulus values: X (amount of red), Y (green), Z (blue) • Color is specified by trichromatic coefficients

8. CIE Chromaticity Diagram

9. Typical color gamut

10. 6.2 Color Models • A color model: color space, color system • Each color is represented by a single point in a color space. • The RGB Color Model

11. Generating an RGB image B G R

12. Full rendition of RGB color requires 24 bits. • Safe RGB colors: a subset of full RGB colors (256 colors) • Structure: 40 different, 216 common (standard) • Pure red (R=255, G=0, B=0): FF0000, white: FFFFFF

13. The CMY and CMYK Color Models

14. The HSI Color Model • RGB: good for cones’ red, green, and blue perception • CMY(K): good for hardware (printer) implementation • How human interprets a color? • Not as a combination (percentage) of three primary components. • But as a combination of hue, saturation, and brightness • HSI Color Model • Decouples intensity (I) from color-carrying components (H, S)

15. Detail description of HIS model

17. RGB to HSI • RGB values are normalized, θ is measured w.r.t. the red axis • HSI to RGB • 0≤H<120 (RG sector) • 120≤H<240 (GB sector) • 240≤H<360 (BR sector)

19. 6.3 Pseudocolor Image Processing6.3.1 Intensity Slicing

20. 6.3 Pseudocolor Image Processing • Intensity Slicing

21. General (P-level) slicing

24. 6.3.2 Gray Level to Color Transformations • Psuedo color coding with a single gray level image

26. Pseudocolor coding with multiple monochrome images

28. 6.4 Basics of Full-Color Image Processing

29. 6.5 Color Transformations6.5.1 Formulation

30. 6.5 Color Transformations • Formulation

31. Reduce the intensity of a color image

32. Color Complement

33. Color Slicing • Purpose: highlighting a specific range of color • Method: extend the gray level slicing techniques

34. One simple approach • Assume the colors of interest are enclosed by a cube (or hypercube for n>3) of width W and centered at the centroid of the cube • Or if a sphere is used instead

35. Tone and Color Corrections • Device independent color model relates the gamuts of the monitors and out devices to one another. • Color management system (CMS): CIE L*a*b* color components • CIE L*a*b* • Colorimetric: colors perceived as matching are encoded identically • Perceptually uniform • Device independent • Decoupling intensity (L) and color (a and b)

37. Histogram Processing • Independent histogram equalization of each color component results in erroneous color.

38. 6.6 Smoothing and Sharpening • Color Image Smoothing

39. 6.6.1

40. Color Image Sharpening

41. 6.7 Color Segmentation • Segmentation in HSI Color Space

42. Segmentation in RGB Vector Space

43. Color Edge Detection

46. 6.8 Noise in Color Images

49. 6.8 Color Image Compression

50. HW05 • Problems in Chapter 5 (pp. 389-393) • 5.29, 5.30, 5.31, 5.33, 5.34 • Problems in Chapter 6 (pp. 456-460) • 6.5, 6.6, 6.10, 6.12, 6.18, and 6.23