comp 319 lecture 02 introduction to multimedia comppguting

COMP 319 Lecture 02

Introduction to Multimedia Computingp g

Fiona Yan LiuDepartment of Computing

The Hong Kong Polytechnic University

Learning Outputs of Lecture 01Learning Outputs of Lecture 01

I d i l i di h l Introduction to multimedia technology What is multimedia History of multimedia technology History of multimedia technology Reference reading: Chapter 1

Introduction to html Introduction to html What is html Html document with web generation Reference reading: Chapter 1

Introduction to COMP319 http://www.comp.polyu.edu.hk/~csyliu/course/comp319/main.html

Lecture 02: Color Model and Human VisionSept. 11, 2014 2

Outline of Lecture 02Outline of Lecture 02

F d t l f i d t t ti Fundamentals of image data representation Standard image

Gray‐level image Gray level image Bitmap and bitplane

Color image Light and human vision Different color models 24‐bit color image and 8‐bit color image 24‐bit color image and 8‐bit color image Histogram and color lookup tables

Some popular image file formatsp p g Reference reading

Chapter 3 & Chapter 4


Fundamentals of Image Data Representation

f l Images consist of pixels The smallest discrete component of an image on the screen

Image resolution The number of pixels in a digital image

Standard imagesg Illustrate algorithms and compare the performance Lena: for gray‐level image generallyg y g g y Baboon: for color image generally


Standard ImagesStandard Images

Lena: Image Resolution is 256 * 256

Baboon: Image Resolution is 512* 512




Graylevel image Gray level image Bitmap and bitplane





Gray Level ImageGray‐Level Image

Image is represented using luminance information only 8‐bit gray‐level image

Each pixel has a gray value between 0 and 25524-bit color Image Gray Image


Binary ImageBinary Image

Each pixel is stored as a single bit 0/1 Each pixel is stored as a single bit 0/1. Also referred as 1‐bit image Use for the pictures containing simple graphics or textp g p g p


Bitmap of Gray level ImageBitmap of Gray‐level Image

The two‐dimensional array of pixel values that represent the images/graphicsp g /g p


Bitplane of 8 bit imageBitplane of 8‐bit image

Bi l Bitplanes Consider the 8‐bit image as a set of 1‐bit bitplanesE h l i t f 1 bit t ti f th Each plane consists of a 1‐bit representation of the image


Light and PrismLight and Prism

h l Light is an electromagnetic wave White light contains all the colors of a rainbow

Sir Isaac Newton's experiment


Light and Spectral Power Distribution

Most light sources produce contributions over many wavelengths Most light sources produce contributions over many wavelengths SPD shows the relative amount of light energy

Spectral power distribution of daylight


The Color of the LightThe Color of the Light

Th l f th li ht i h t i d b th l th The color of the light is characterized by the wavelength of the light

Short wavelengths produce a blue sensation, long g p , gwavelengths produce a red one

Spectral power distribution of daylight


Human VisionHuman Vision

Humans cannot detect all light Visible light is an electromagnetic wave in the range g g g400 nm to 700 nm

Human vision is formedHuman vision is formed Sensor: Eye Most sensitive to red(R) green(G) and blue(B) Most sensitive to red(R), green(G), and blue(B).

Processor: Brain R G B R,G,B

R‐G, G‐B, and B‐R


Human RetinaHuman Retina

f f d d Human retina consists of an array of rods and three kinds of cones

Rod Detect gray‐level information

Cones Three kinds of cones are used to detect R,G,B, , The proportions of R,G,B cones are 40:20:1

The eye is most sensitive to light in the middle The eye is most sensitive to light in the middle of the visible spectrum


RGB Color ModelRGB Color Model

Th Three primaries Red, green and blue

Additive color model When two light beams impinge on a target, their colors addadd


24 bit Color Image of RGB Model24‐bit Color Image of RGB Model24-bit color Image Red Channel

R 8‐bit

G 8 bit 8‐bit

B Blue Channel Green Channel

8bit


24 bit Color Image24‐bit Color Image

Each pixel is represented by three bytes, usually RGB. Supports 256*256*256, totally 16.8‐million possible combined color.

Storage: for image resolution of 640*480, needs 921.6KB

Some 24‐bit color images are stored as 32‐bit imageimage Extra byte of data for special‐effect information


Color Lookup Tables (CLTs)Color Lookup Tables (CLTs)

U i d d l i t d f 24 bit Use index or code value instead of 24‐bit color information for each pixelC l l k h bl k ll f ll Color lookup the table works well for small combinations


Image HistogramImage Histogram

R f t th b bilit f ti f th i Refers to the probability mass function of the image intensities

5

6Original data: 4.2 3.4 0 4.5 4.2 1.7 6 3.8 3

The number of containers is equal to six

2

3

4

1

2

1 2 3 4 5 6 7 80

1

0 1 2 3 4 5 60

4The number of containers is equal to three

3

4

1

2


1 2 30

8 bit Color Image8‐bit Color Image

Di id h RGB b i l lid i h Divide the RGB cube into equal slides in each dimension R: 3 bit; G: 3 bit; B: 2 bit; R: 3‐bit; G: 3‐bit; B: 2‐bit; Edge artifacts

24-bit color image 8-bit color image


L*a*b* Color ModelL*a*b* Color Model

P l if Perceptual uniform Color differences a human

perceives as equal correspond to Euclidean distances

RGB color model is not perceptual uniformperceptual uniform

L*a*b* color model is perceptual uniform

L* l i L*: luminance a*: red/green balance b*: green/blue balanceg /


RGB and CIE L*a*bRGB and CIE L*a*b


HSV Color ModelHSV Color Model

A f l f Approximation of perceptual uniform Hue: position in the color spectrum Saturation: the vividness of a color Value: the brightness of the color Value: the brightness of the color


YUV Color ModelYUV Color Model

B d i JPEG Be used in JPEG Y: luminance value

Luma Y’: (gamma‐corrected)(g ) U and V: chrominance

The difference between a color and a reference white at the same luminanceluminance

U = B’ – Y’ V = R’ –Y’


Gamma CorrectionGamma Correction

’ l h d Human’s response is not linear to the driving voltage of display device


YUV Color ModelYUV Color Model


YC C color modelYCbCr color model

YC C color model is closely related to the YUV YCbCr color model is closely related to the YUV transformation Be used in MPEG Be used in MPEG


YIQ Color ModelYIQ Color Model

YIQ is used in NTSC color TV broadcasting Y’ in YIQ is the same as in YUV I and Q are a rotated version of U and V

U and V are rotated by 330


YIQ color modelYIQ color model

I and Q in YIQ model

U and V in YUV d lYUV model


Subtractive Color ModelSubtractive Color Model

Addi i l d l Additive color model When two light beams impinge on a target, their colors addS bt ti l d l Subtractive color model For ink deposited on paper, opposite situation holds

RGB color model CMY color model


CMY Color Model and CMYK Color Model

CMY color model consist of CMY color model consist of Cyan(C), Magenta(M) and Yellow(Y) Subtractive color primaries

Transformation from RGB to CMY The transformation is invertible

CMYK color model Add one color: real black Using black ink is cheaper and better than using the mixing colored

ink


Some Popular Image File FormatsSome Popular Image File Formats

BMP BMP BitMap Mainly use RGB color modely

Gif Graphics Interchange Format

I t t f t b f it hi t i l ti t th WWW Important formats because of its historical connection to the WWW and HTML

PDF Portable Document Format Include compression

JPEG JPEG Joint Photographic Experts Group Currently the most important common file format


Announcement of Further Arrangement

Lab Lab Thur. 16:30 – 17:20 PQ604B Tue. 9:30 – 10:20 PQ606

Quiz Quiz The first quiz will be next Thur. Sept. 18 8:30 – 9:00 Cover Lecture 1 ‐ 2

Form of the questions Form of the questions True or False Answer the questions Calculation

Lecture 02: Color Model and Human VisionSept. 11, 2014

Calculation

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comp 319 lecture 02 introduction to multimedia comppguting

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