Color Standards A pixel color is represented as

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Color Standards • A pixel color is represented as a point in 3 -D

Color Standards • A pixel color is represented as a point in 3 -D space. Axis may be labeled as independent colors such as R, G, B or may use other independent indicators such as Hue, lightness, saturation. • RBG, HSV, HSL are the most popular. 240 -373 Image Processing 1

RGB • RGB (Red green blue) – For CRT display (TV, computer monitor) –

RGB • RGB (Red green blue) – For CRT display (TV, computer monitor) – Additive combination of r g and b 240 -373 Image Processing 2

HSV • HSV (Hue Saturation Value) Hue is effectively a measure of the wavelength

HSV • HSV (Hue Saturation Value) Hue is effectively a measure of the wavelength of the main color. It has a value between 0 -255 (0 -360 o, Red = 0 o, Green=120 o and Blue=240 o. ) Hue can be calculated from RGB values as follows: Rh = R - min (R, G, B) Gh = G - min (R, G, B) Bh = B - min (R, G, B) At least on of these values is 0. Hue value is in between. 240 -373 Image Processing 3

HSV For example if Rh = 0 Hue angle = (240 x. Bh +

HSV For example if Rh = 0 Hue angle = (240 x. Bh + 120 x. Gh ) / (Bh + Gh) If two are zero, then hue is the angle corresponding to the third non-zero color. If three are zero, then there is no color hue. The monitor will display a gray level (between black and white). 240 -373 Image Processing 4

Saturation • Saturation is the amount of pure hue in the final color If

Saturation • Saturation is the amount of pure hue in the final color If Saturation = 0, final color is without hue ( white light only) If Saturation = 255, no white light in final color Saturation = (max(R, G, B) - min (R, G, B)) / max(R, G, B) 240 -373 Image Processing 5

Value • Value (brightness) is a measure of the intensity of the brightest component

Value • Value (brightness) is a measure of the intensity of the brightest component and given by Value = max(R, G, B) 240 -373 Image Processing 6

The HSV Model 240 -373 Image Processing 7

The HSV Model 240 -373 Image Processing 7

HSV Example Given R=100, G=200, B= 40, convert this RGB color model to HSV.

HSV Example Given R=100, G=200, B= 40, convert this RGB color model to HSV. Rh = 100 - min(100, 200, 40) = 60 Gh = 200 - min (100, 200, 40) = 160 Bh = 40 - min (100, 200, 40) = 0 H = (60*0 + 160*(120*256/360))/(60+160( 240 -373 Image Processing 8

HSV Example (Continued( Saturation = (max(R, G, B) - min (R, G, B)) /

HSV Example (Continued( Saturation = (max(R, G, B) - min (R, G, B)) / max(R, G, B) = (max(100, 200, 40)-min(100, 200, 40/(( max(100, 200, 40( 200 -40)/200) = %80 = 0. 8 = Saturation = 80*256/100 = 204 Value = max(100, 200, 40)200 = %78 = 100/256*200 = 240 -373 Image Processing 9

240 -373 Image Processing 10

240 -373 Image Processing 10

HLS (Hue Lightness Saturation) • Similar to HSV except that the hue angle start

HLS (Hue Lightness Saturation) • Similar to HSV except that the hue angle start at Blue = 0 o, and the model is double cone with a lightness axis going from L=0 (black) to L=1 (white) • For HLS, hue is calculated the same way as for HSV model except Blue = 0 and lightness and saturation are given by: • Lightness = (max(R, G, B) - min(R, G, B))/2 (max+min)/(max-min) if L <= 0. 5 (max-min)/(2 -max-min) otherwise • Saturation = 240 -373 Image Processing 11

HLS 240 -373 Image Processing 12

HLS 240 -373 Image Processing 12

The Human Vision • Better than any camera yet developed • Eye has 2

The Human Vision • Better than any camera yet developed • Eye has 2 classes of discrete light receptors; Cones and rods • 6 -7 million cones, sensitive to bright light • >= 75 Million rods, sensitive to light intensity but not color • eye color perceptions 240 -373 Image Processing 15

Color Additive • Problem with CRT 240 -373 Image Processing 16

Color Additive • Problem with CRT 240 -373 Image Processing 16