lecture 8. color image processing - ufrj

Lecture 8. Color Image Processing

EL512 Image Processing

Dr. Zhu [email protected]

Note: Part of the materials in the slides are from Gonzalez’s Digital Image Processing and Onur’s lecture slides

Lecture Outline

Fall 2003 EL512 Image Processing Lecture 8,

• 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

Light is part of the EM wave


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 http://www.macula.org/anatomy/retinaframe.html

Frequency Responses of Cones


ybgridaCC ii ,,,,)()( == ∫ λλλ

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

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

RGB vs CMY


Magenta = Red + BlueCyan = Blue + GreenYellow = Green + Red

Magenta = White - GreenCyan = White - RedYellow = White - Blue

A Color Image


Red

Green Blue

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

– Only two chromaticity coefficients are necessary to specify the chrominance of a light.

.,,ZYX

ZzZYX

YyZYX

Xx++

=++

=++

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1=++ zyx

CIE Chromaticity Diagram


CIE (Commission Internationale de L’Eclairage, International Commission on Illumination ) system of color specification

x axis: redy axis: green

The point marked with GREENx: 25%, y: 62%, z: 13%.

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

RGB Color Model


RGB 24-bit color cube

CMY and CMYK Color Models


• 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.

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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.

The HSI Color Model


Conversion Between RGB and HSI


• Converting color from RGB to HSI

• Converting color from HSI to RGB

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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

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703.1106.10.1649.0272.00.1

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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

Comparison of Different Color Spaces


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

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

Color Image Display


Input Output

RedLUT

RedGun

GreenLUT

RedGun

BlueLUT

RedGun

Red signal

01…

255

Green signal

Blue signal

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.

Gamma Correction


• The intensity to voltage response curve of the computer monitor is not linear.

• Gamma correction

Sample Input to Monitor

Output from Monitor

Graph of Input

Graph of Output L=V2.5

Sample Input to Monitor

Monitor Output

Gamma corrected Input

Graph of Input

Graph of Output

Graph of Correction L’=L1/2.5

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”

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 … … …

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

Illustration of the Vector Quantization


y y

x x

Codebook size: 25

Vector QuantizationUniform Quantization

Example of Color Image Quantization


24 bits -> 8 bits

Uniform quantization(3 bits for R,G, 2 bits for B)Adaptive (non-uniform) quantization

(vector quantization)

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).

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 onlyOriginal value (R,G, or B)

Dithering value

Dithered value

Example of Color Dithering


8 bit uniformwith diffusion dithering

8 bit uniformwithout dithering

Demo Using Photoshop


• Show quantization results with different methods using “image->mode->index color”

Pseudo Color Image


• Why?– 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.

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.

Intensity Slicing


f0 =0

C3

C2

C1

f2f1 f3 f4

C4

Gray level

Color

Pixels with gray-scale (intensity) value in the range of (f i-1 , fi) are rendered with color Ci

Example


Another Example


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

An Example


Example


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.

Example of Color Image Enhancement


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 81-160, color blue for values 161-255. In matlab, you can use the function colormap to change the colormap and use imshowto 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 0-256. Display the original and quantized color image using the original colormapassociated 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.

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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.

lecture 8. color image processing - ufrj

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