1 Color Processing Introduction Color models Color image processing

2 Definition of Color Physical aspects –color is a part of magnetic spectrum of visible light. Perceptual aspects –amount made up by varying R, G and B colors. –cone cells in human eyes detecting color (one for each R, G and B color) –R, G, B = primary color

3 Primary and Secondary Colors Primary colors: the color consist of 1 primary color Secondary colors: the color consist of 2 primary colors

4 Primary and Secondary Colors (2)

5 Color Model A.k.a. color space, color system Specify a color as a point in some standard coordinate Popular color models: –RGB color models –HSV color models –YIQ color models (NTSC standard) –LUV and LAB color models

6 RGB Color Model Cartesian coordinate system Stand for RED, GREEN and BLUE color

7 Pixel Depth Pixel depth: #bit represented RGB image –E.g. 24-bit RGB color image: 8-bit for each color. Able to represent (2 8 ) 3 color Full-color image = 24-bit RGB color image R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, 2 nd Ed., Prentice Hall, 2002.

8 Safe RGB Colors A.k.a all-system-safe colors, safe Web colors, safe browser color Set of the color that are likely to be reproduced color independent of the hardware Set of 216 colors (the other 40 are reproduced differently by various OS) Value for RGB: 0, 51, 102, 153, 204, 255 Show in Hex format RRGGBB

9 Safe Color Diagram and Cube Color only on the surface of the cube R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, 2 nd Ed., Prentice Hall, 2002.

10 HSV Color Model Hue: true color attribute Saturation: amount that the color is diluted by white –pure red  high saturation –light red  low saturation Value: degree of brightness

11 HSV Color Space

12 HSV  RGB H’RGB 0VTP 1QVP 2PVT 3PQV 4TPV 5VPQ All values are normalized.

13 HSV: MATLAB Command RGB  HSV –MATLAB: rgb2hsv(Red, Green, Blue); HSV  RGB –MATLAB: hsv2rgb(Hue, Saturation, Value);

14 RGB Image VS HSV Image RGB Image Hue Image Saturation Image (white : low) Value Image

15 YIQ Color Space Y : luminance, brightness I, Q: chrominance (color information)

16 YIQ: MATLAB Command RGB  YIQ –MATLAB: rgb2ntsc(Red, Green, Blue); YIQ  RGB –MATLAB: ntsc2rgb(Y, I, Q);

17 RGB Image VS YIQ Image RGB Image Y Image I Image Q Image

18 MATLAB Structure 3-dimensional matrix: –[row, column, color space] RGB(HSV, YIQ): –red (hue, Y) components: [..,.., 1] –green (saturation, I) components: [..,.., 2] –blue (value, Q) components: [..,.., 3]

19 Contrast Enhancement Use histogram manipulation (E.g. histogram equalization) on only intensity component. Processing on RGB matrix leads to color distortion.

20 Histogram Equalization on RGB BEFOREAFTER

21 Spatial Filtering Blurring: any are fine –average filter on RGB components –average filter on intensity(Y) components High-pass filter (E.g. unsharp) –process on intensity components General: work on intensity components

22 Smoothed Lena Blame the reddish tone on the scanner!!! R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, 2 nd Ed., Prentice Hall, 2002.

23 Noise Reduction Depended on where noise is generated. –generated in RGB spaces: reduce noise in RGB matrix –generated in brightness space: reduce noise in intensity (Y) components

24 Edge Detection Use edge detection on intensity component only Use edge detection on R, G and B components separately and join the result