{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}


color.slides.printing.2 - CS 450 Introduction to Digital...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: CS 450: Introduction to Digital Signal and Image Processing Color Processing Light and Wavelengths • Visible light is in the range 400 nm (blue) to 700 nm (red). Perception of Light • Cones have three different kinds of color-sensitive pigments, each responding to a different range of wavelengths. • These are roughly “red”, “green”, and “blue” in their peak response but each responds to a wide range of wavelengths. • The combination of the responses of these different receptors gives us our color perception. • This is called the tristimulus model of color vision. Cones and Wavelengths (From G&W, individual cone responses normalized) Cones and Wavelengths • The sensitivity and number of the three types of cones are different • More sensitive overall to green and red than to blue Relative responses of the three different cones Luminous Efficiency Function • The luminous efficiency function combines the responses of all three to measure perceived brightness for different wavelengths RGB Color Model • Simplest model is just to store red, green, and blue values • Colors can be thought of as points in a RGB cube RGB Color Channels “Web Safe” Colors Primaries and Secondaries • Primary colors: ones mixed to make other colors • Secondary colors: pairwise combinations of primaries • Can be additive or subtractive CMY Model • Subtractive media absorb rather than emit light – Real-world objects – Paint – Printing ink • The perceived color is what is not absorbed (reflected) • CMY based on subtractive primaries: cyan (C), magenta (M), and yellow (Y) CMYK Model • Problem with CMY model: – Because of imperfect primaries, can’t entirely absorb all colored light (i.e., make black) – Usually produces a dark greyish brown • Solution: add black (K) as a fourth primary • CMYK most common model for printers Luminance and Chromaticity • RGB and CMYK aren’t the most intuitive model of colors. • Artists usually think of dark/light and color as two different things: – Luminance (brightness) – Chromaticity (color) Luminance • Technically, – Luminance = incoming light – Brightness = perceived incoming light • Many color models make the luminance or brightness an explicit component of the color model though by varying names: – – – – – – Luminance Intensity Brightness Lightness Luma Value Chromaticity • Requires two parameters – Usually: • Hue • Saturation The dominant wavelength How pure that color is (ratio of color to white) – Some models use different chromaticity parameters • Examples: – Red vs. Blue? – Red vs. Pink? – Dark red vs. Bright red? Tints and Shades • Lightening or darkening colors: – Tint: adding more white to a color – Shade: adding more black to a color • Tints and shades are not inverses! – Why not? The CIE RGB Model • Standardized “red”, “green”, and “blue” to try to match perception: 438.1, 546.1, and 700 mn R = Km G = Km B = Km Ú L(l) r(l) dl Ú L(l) g(l) dl Ú L(l) b(l) dl Notice negative weight of red component for † wavelengths near 500 nm—can’t physically mix The CIE XYZ Model (1931) • Replaces R, G, B with “imaginary” primaries X, Y, Z – Don’t correspond to single wavelengths – Weights are always positive – More saturated than monochromatic light – Can model all physically realizable colors X = Km Y = Km Z = Km † Ú L(l) x (l) dl Ú L(l) y (l) dl Ú L(l) z (l) dl Normalizing CIE XYZ Model • Normalize X, Y, Z so that we can talk about chromaticity: X X +Y + Z Y y= X +Y + Z Z z= X +Y + Z x= † Notice that x + y + z = 1 forms a plane in color space — can just use x and y (because z = 1 - x - y) CIE Chromaticity Diagram Gamut Color Gamuts • A color gamut is the space of colors spanned by a set of primaries • No three physical primaries can span the entire space of physically realizable colors – Use “negative” weights for some colors, or – Use “imaginary” primaries that lie outside the color space – Both are physically impossible • Better with more primaries, but still not able to span the entire color space – Some color printers use more than just CMYK Device Primaries and Gamuts • Different devices have different primaries and different gamuts – Scanners – Monitors – Printers • Two major problems working with color: – Calibration: “talking the same color language” – Out-of-gamut limitations NTSC YIQ Model • The National Television Standards Committee (NTSC) standard uses a color model called YIQ: – Y luminance – I and Q chromaticity ÈY ˘ È0.299 0.587 0.114˘ÈR ˘ Í˙ Í ˙Í ˙ ÍI ˙ = Í0.596 -0.275 -0.321˙ÍG˙ ÍQ˙ Í0.212 -0532 0.311˙ÍB ˙ Î˚ Î ˚Î ˚ • Television Signals – Color TV broadcasts are YIQ †– “Black-and-white” (grey) TV uses Y only HSI Color Model • One axis is intensity (luminance) • The plane perpendicular to this axis represents chromaticity – Angle = hue – Distance from center = saturation • Can map this plane using a triangle, hexagon, or circle HSI Color Model HSI Color Model HSI Color Model Other Color Models • Hue-Saturation-Value (HSV) – Like HSI but with only one cone • • • • Hue-Lightness-Saturation (HLS) Hue-Value-Chroma (HVC) CIE LUV CIE La*b* – Attempts to be perceptually linear Pseudocolor (Indexed Color) • For some applications, you may want to store fewer than 24 bits per pixel – Older/less-expensive video buffers – Compression (used in GIF compression) • Use a lookup table to map values in the image buffer to 24-bit color values – Example: 8-bit buffer, 256-color lookup table – The set of displayable colors in the lookup table is often called the palette • Selection of palette from an image is known as color quantization Pseudocolor (Indexed Color) • Can use similar method for mapping grey level image to color Color and Visualization • Color can be used in information visualization to provide an additional dimension Color and Visualization • Specific color meanings: keep it limited • Color scales are hard to interpret – Heat scale is one that of the few that partially works • Limits of color reproduction can change the message Color Image Processing • General approaches – Process RGB color planes separately • Simple • Not always meaningful thinking in RGB – Convert to HSI or other color space with explicit intensity component • Process the intensity component as you normally would • May process the saturation • Generally want to leave the hue alone in this model – Design transformation in intensity/chromaticity space, but implement through direct RGB manipulation Processing RGB Planes Processing Other Components Color Transformations Color Transformations • Can change channels together to transform the intensities Color Balancing • Or you can change channels separately – Can emphasize or de-emphasize one or more channels Color Histogram Equalization • Convert to HSI or similar space • Process – Histogram equalize the intensity plane – Increase the saturation – Leave the hue plane unchanged • Convert back to RGB for display Color Histogram Equalization Summary • Pick a color space that makes sense for your application • If the application is interactive, consider using HSI or other color space with explicit intensity/chromaticity • If you want to process intensities but keep hues the same, work (or at least think) in that kind of color space • Keep physical limitations in mind – Primaries, different color gamuts, etc. • There’s a lot more to color than RGB triplets! ...
View Full Document

{[ snackBarMessage ]}

Ask a homework question - tutors are online