Lecture 8. Color Image Processing

Transcription

1 Lecture 8. Color Image Processing EL512 Image Processing Dr. Zhu Liu Note: Part of the materials in the slides are from Gonzalez s Digital Image Processing and Onur s lecture slides

2 Lecture Outline Color perception and representation Human perception of color Trichromatic color mixing theory Different color representations Color image display True color image Indexed color images Pseudo color images Color image enhancement Fall 2003 EL512 Image Processing Lecture 8, Page 2

3 Light is part of the EM wave Fall 2003 EL512 Image Processing Lecture 8, Page 3

4 Human Perception of Color Retina contains receptors Cones Day vision, can perceive color tone Red, green, and blue cones Rods night vision, perceive brightness only Color sensation Luminance (brightness) Chrominance Hue (color tone) Saturation (color purity) From /retinaframe.html Fall 2003 EL512 Image Processing Lecture 8, Page 4

5 Frequency Responses of Cones Ci = C( λ) ai ( λ) dλ, i = r, g, b, y Fall 2003 EL512 Image Processing Lecture 8, Page 5

6 Illuminating and Reflecting Light Illuminating sources: emit light (e.g. the sun, light bulb, TV monitors) perceived color depends on the emitted frequency Reflecting sources: reflect an incoming light (e.g. the color dye, matte surface, cloth) perceived color depends on reflected frequency (=emitted frequency - absorbed frequency Fall 2003 EL512 Image Processing Lecture 8, Page 6

7 Tri-chromatic Color Mixing Tri-chromatic color mixing theory Any color can be obtained by mixing three primary colors with a right proportion Primary colors for illuminating sources: Red, Green, Blue (RGB) Color monitor works by exciting red, green, blue phosphors using separate electronic guns follows additive rule: R+G+B=White Primary colors for reflecting sources (also known as secondary colors): Cyan, Magenta, Yellow (CMY) Color printer works by using cyan, magenta, yellow and black (CMYK) dyes follows subtractive rule: R+G+B=Black Fall 2003 EL512 Image Processing Lecture 8, Page 7

8 RGB vs CMY Magenta = Red + Blue Cyan = Blue + Green Yellow = Green + Red Magenta = White - Green Cyan = White - Red Yellow = White - Blue Fall 2003 EL512 Image Processing Lecture 8, Page 8

9 A Color Image Red Green Blue Fall 2003 EL512 Image Processing Lecture 8, Page 9

10 Tristimuls Values Tristimulus value The amounts of red, green, and blue needed to form any particular color are called the tristimulus values, denoted by X, Y, and Z. Trichromatic coefficients x = X X + Y + Z, y = X Y + Y + Only two chromaticity coefficients are necessary to specify the chrominance of a light. x + y + z =1 Z, z = X Z + Y + Z. Fall 2003 EL512 Image Processing Lecture 8, Page 10

11 CIE Chromaticity Diagram CIE (Commission Internationale de L Eclairage, International Commission on Illumination ) system of color specification x axis: red y axis: green The point marked with GREEN x: 25%, y: 62%, z: 13%. Fall 2003 EL512 Image Processing Lecture 8, Page 11

12 Color Models Specify three primary or secondary colors Red, Green, Blue. Cyan, Magenta, Yellow. Specify the luminance and chrominance HSB or HSI (Hue, saturation, and brightness or intensity) YIQ (used in NTSC color TV) YCbCr (used in digital color TV) Amplitude specification: 8 bits per color component, or 24 bits per pixel Total of 16 million colors A 1kx1k true RGB color requires 3 MB memory Fall 2003 EL512 Image Processing Lecture 8, Page 12

13 RGB Color Model RGB 24-bit color cube Fall 2003 EL512 Image Processing Lecture 8, Page 13

14 CMY and CMYK Color Models Fall 2003 EL512 Image Processing Lecture 8, Page 14 Conversion between RGB and CMY Equal amounts of Cyan, Magenta, and Yellow produce black. In practice, this produce muddy-looking black. To produce true black, a fourth color, black is added, which is CMYK color model , = = Y M C B G R B G R Y M C

15 HSI Color Model Hue represents dominant color as perceived by an observer. It is an attribute associated with the dominant wavelength. Saturation refers to the relative purity or the amount of white light mixed with a hue. The pure spectrum colors are fully saturated. Pink and lavender are less saturated. Intensity reflects the brightness. Fall 2003 EL512 Image Processing Lecture 8, Page 15

16 The HSI Color Model Fall 2003 EL512 Image Processing Lecture 8, Page 16

17 Conversion Between RGB and HSI Fall 2003 EL512 Image Processing Lecture 8, Page 17 Converting color from RGB to HSI Converting color from HSI to RGB [ ] [ ] ] [ 3 1 )],, [min( ) ( 3 1 ) )( ( ) ( ) ( ) ( 2 1 cos, B G R I B G R B G R S B G B R G R B R G R with G B if G B if H + + = + + = + + = > = θ θ θ ) ( 1 ) cos(60 cos 1 ) (1 B R G H H S I R S I B + = + = = RG sector (0 H<120) ) ( 1 120)) ( cos(60 120) cos( 1 ) (1 G R B H H S I G S I R + = + = = GB sector (120 H<240) ) ( 1 240)) ( cos(60 240) cos( 1 ) (1 B G R H H S I B S I G + = + = = BR sector (240 H<360)

18 YIQ Color Coordinate System YIQ is defined by the National Television System Committee (NTSC) Y describes the luminance, I and Q describes the chrominance. A more compact representation of the color. YUV plays similar role in PAL and SECAM. Conversion between RGB and YIQ Y = I Q R G, B R 1.0 = G 1.0 B Y I Q Fall 2003 EL512 Image Processing Lecture 8, Page 18

19 Criteria for Choosing the Color Coordinates The type of representation depends on the applications at hand. For display or printing, choose primary colors so that more colors can be produced. E.g. RGB for displaying and CMY for printing. For analytical analysis of color differences, the difference in the trisitumulus values are linearly related to the chrominance differences. HSI is more suitable. For transmission or storage, choose a less redundant representation, eg. YIQ or YUV Fall 2003 EL512 Image Processing Lecture 8, Page 19

20 Comparison of Different Color Spaces Fall 2003 EL512 Image Processing Lecture 8, Page 20

21 Demo Using Photoshop Show the RGB, CMY, HSI models Using the window->info tool and the window>show color tool (in show color, click on right arrow button to choose different color sliders) Sample image: RGBadd, CMYsub Fall 2003 EL512 Image Processing Lecture 8, Page 21

22 Color Image Display and Printing Display: Need three light sources projecting red, green, blue components respectively at every pixel Analog display: raster scan Digital display: directly projecting at all pixel locations Printing: Need three (or more) color dyes (Cyan, Magenta, Yellow, and Black) Analog printing Digital printing Out of gamut color Fall 2003 EL512 Image Processing Lecture 8, Page 22

23 Color Image Display Input Output Red signal Red LUT Red Gun Green signal Green LUT Red Gun Blue signal Blue LUT Red Gun Fall 2003 EL512 Image Processing Lecture 8, Page 23

24 Color Gamut Each color model has different color range (or gamut). RGB model has a larger gamut than CMY. Therefore, some color that appears on a screen may not be printable and is replaced by the closest color in the CMY gamut. Fall 2003 EL512 Image Processing Lecture 8, Page 24

25 Gamma Correction The intensity to voltage response curve of the computer monitor is not linear. Sample Input to Monitor Output from Monitor Gamma correction Sample Input to Monitor Graph of Input Graph of Output L=V 2.5 Graph of Input Gamma corrected Input Graph of Correction L =L 1/2.5 Monitor Output Graph of Output Fall 2003 EL512 Image Processing Lecture 8, Page 25

26 Demo with Photoshop Using photoshop to show how to replace a out of gamut color by its closest in-gamut color. Choose window->show swatch, choose blue Fall 2003 EL512 Image Processing Lecture 8, Page 26

27 Color Quantization Select a set of colors that are most frequently used in an image, save them in a look-up table (also known as color map or color palette) Any color is quantized to one of the indexed colors Only needs to save the index as the image pixel value and in the display buffer Typically: k=8, m=8 (selecting 256 out of 16 million) Input index (k bits) Red color (m bits) Green color (m bits) Blue color (m bits) Index 1. Index 2^k Fall 2003 EL512 Image Processing Lecture 8, Page 27

28 Uniform vs. Adaptive Quantization Uniform (scalar quantization) Quantize each color component uniformly E.g. 24 bit-> 8 bit can be realized by using 3 bits (8 levels) for red, 3 bits (8 levels) for green, 2 bits (4 levels) for blue Do not produce good result Adaptive (vector quantization) Treating each color (a 3-D vector) as one entity. Finds the N colors (vectors) that appear most often in a given image, save them in the color palette (codebook). Replace the color at each pixel by the closest color in the codebook The codebook (I.e. color palette) varies from image to image -> adaptive Fall 2003 EL512 Image Processing Lecture 8, Page 28

29 Illustration of the Vector Quantization y Codebook size: 25 y x x Uniform Quantization Vector Quantization Fall 2003 EL512 Image Processing Lecture 8, Page 29

30 Example of Color Image Quantization 24 bits -> 8 bits Adaptive (non-uniform) quantization (vector quantization) Uniform quantization (3 bits for R,G, 2 bits for B) Fall 2003 EL512 Image Processing Lecture 8, Page 30

31 Web Colors: 216 Safe RGB Colors These colors are those that can be rendered consistently by different computer systems. They are obtained by quantizing the R,G,B component independently using uniform quanitization. Each component is quantized to 6 possible values: 0(0x00), 51(0x33), 102(0x66), 153(0x99), 204(0xCC), 255(0xFF). Fall 2003 EL512 Image Processing Lecture 8, Page 31

32 Color Dithering Color quantization may cause contour effect when the number of colors is not sufficient Dithering: randomly perturb the color values slightly to break up the contour effect fixed pattern dithering diffusion dithering (the perturbed value of the next pixel depends on the previous one) Developed originally for rendering gray scale image using black and white ink only Original value (R,G, or B) Dithered value Dithering value Fall 2003 EL512 Image Processing Lecture 8, Page 32

33 Example of Color Dithering 8 bit uniform without dithering 8 bit uniform with diffusion dithering Fall 2003 EL512 Image Processing Lecture 8, Page 33

34 Demo Using Photoshop Show quantization results with different methods using image->mode->index color Fall 2003 EL512 Image Processing Lecture 8, Page 34

35 Why? Pseudo Color Image Human eye is more sensitive to changes in the color hue than in brightness. How? Use different colors (different in hue) to represent different image features in a monochrome image. Fall 2003 EL512 Image Processing Lecture 8, Page 35

36 Pseudo Color Display Intensity slicing: Display different gray levels as different colors Can be useful to visualize medical / scientific / vegetation imagery E.g. if one is interested in features with a certain intensity range or several intensity ranges Frequency slicing: Decomposing an image into different frequency components and represent them using different colors. Fall 2003 EL512 Image Processing Lecture 8, Page 36

37 Intensity Slicing Color C 4 C 3 C 2 C 1 f 0 =0 f 1 f 2 f 3 f 4 Gray level Pixels with gray-scale (intensity) value in the range of (f i-1, f i ) are rendered with color C i Fall 2003 EL512 Image Processing Lecture 8, Page 37

38 Example Fall 2003 EL512 Image Processing Lecture 8, Page 38

39 Another Example Fall 2003 EL512 Image Processing Lecture 8, Page 39

40 Pseudo Color Display of Multiple Images Display multi-sensor images as a single color image Multi-sensor images: e.g. multi-spectral images by satellite Fall 2003 EL512 Image Processing Lecture 8, Page 40

41 An Example Fall 2003 EL512 Image Processing Lecture 8, Page 41

42 Example Fall 2003 EL512 Image Processing Lecture 8, Page 42

43 Color Image Enhancement Enhance each primary color component independently using the techniques for monochrome images Will change the color hue of the original image Convert the tri-stimulus representation into a luminance / chrominance representation, and enhance the contrast of the luminance component only. Use HSI representation, where I truly reflects the luminance information. Fall 2003 EL512 Image Processing Lecture 8, Page 43

44 Example of Color Image Enhancement Fall 2003 EL512 Image Processing Lecture 8, Page 44

45 Example of Color Image Enhancement Fall 2003 EL512 Image Processing Lecture 8, Page 45

46 Example of Color Image Enhancement Fall 2003 EL512 Image Processing Lecture 8, Page 46

47 Homework 1. (Computer Assignment) Write a program which first performs high pass filtering (you can use matlab func conv2 for this part) of an input gray scale image using the following filter: Scale the filtered image to range between 0 and 255. Then displays the filtered image using 3 pseudo colors, using the following color transformation: Color Red for values 0-80, Color Green for values , color blue for values In matlab, you can use the function colormap to change the colormap and use imshow to display an image using a specified colormap. Comment on the visual effect, e.g. each color represents what attributes of the image? 2. (Computer Assignment) Choose a 24 bit RGB color image, perform the following operations: 1) convert it to YIQ format, save the resulting images in three separate files (for Y, I and Q components respectively), each with 8 bits/pixel; 2) perform histogram equalization to the Y image; 3) convert the enhanced Y image and the original I and Q image back to the RGB image. View the original and enhanced color RGB images and comment on your observations. 3. (Computer Assignment) Choose a 24 bit color RGB image, quantize the R, G, and B components to 3, 3, and 2 bits, respectively, using a uniform quantizer in the range Display the original and quantized color image using the original colormap associated with the image. Comment on the difference in color accuracy. Make sure you use a computer that has a 24 bit color display, and the test image has good color contrast. Fall 2003 EL512 Image Processing Lecture 8, Page 47

48 Reading Prof. Yao Wang s Lecture Notes, Chapter 6. R. Gonzalez, Digital Image Processing, Chapter 6. A. K. Jain, Fundamentals of Digital Image Processing, Section 3.7 ~ 3.11, 7.7 ~ 7.8. Fall 2003 EL512 Image Processing Lecture 8, Page 48