MULTIMEDIA SYSTEMS
|
|
- Valentine Tate
- 5 years ago
- Views:
Transcription
1 1 Department of Computer Engineering, g, Faculty of Engineering King Mongkut s Institute of Technology Ladkrabang MULTIMEDIA SYSTEMS Pakorn Watanachaturaporn, Ph.D. pakorn@live.kmitl.ac.th, pwatanac@gmail.com Chapter 2 Digital Image Representation 2
2 Introduction Digital images are created by three basic methods Bitmapping (also called pixmaps or raster graphics) Created with a pixel-by-pixel specification of points of color Vector graphics Use object specification and mathematical equations to describe shapes to which color are applied Procedural modeling (also called algorithmic art) A computer program uses some combination of mathematics, logic, control structures, and recursion to determine the color of pixels and thereby the content of the overall picture 3 Bitmaps Digitization A bitmap is two-dimensional array of pixels describing a digital image Three main ways to create a bitmap 1. Software 2. Reproductions of scenes and objects a snapshot from a traditional camera, then scan the photograph with a digital image 3. Shoot the image with a digital camera 4
3 Bitmap 5 Bitmaps Digitization A matter of the color model used and the corresponding bit depth Both sampling and quantization can introduce error Not enough samples, the image will lack clarity A low bit depth can result in patchiness of color 6
4 Bitmap Pixel Dimensions, Resolution, and Image Size Logical pixel a bitmap Physical pixel a computer display Pixel dimensions Defined as the number of pixel horizontally and vertically denoted w h ; e.g., Resolution Defined as the number of pixels in an image file per unit of spatial measure E.g., pixels per inch, ppi Resolution of a printer dots per inch, dpi 7 Bitmap Pixel Dimensions, Resolution, and Image Size Image size Defined as the physical dimensions of an image when it is printed out or displayed on a computer For an image with resolution r and pixel dimensions w h where w is the width and h is the height, the printed image size a b is given by a w = and b= r h r 8
5 Bitmap Pixel Dimensions, Resolution, and Image Size Cropping Cutting off part of the picture, discarding the unwanted pixels Resampling Changing the number of pixels in an image Involving some kind of interpolation, averaging, or estimating Cannot improve the quality of an image Upsampling Increase the pixel dimensions Downsampling Decrease the pixel dimensions 9 Frequency in Digital Images Considering an image as a function that can be represented in a graph such that y = f ( x) x = the position of one point of color f = a function over the spatial domain y = the color value at position x 10
6 Frequency in Digital Images In the realm of digital imaging, frequency refers to the rate at which color values change 11 Frequency in Digital Images In a digital image we need to discretize them 12
7 Frequency in Digital Images 13 Discrete Cosine Transform Fourier theory Any complex periodic waveform can be equated to an infinite sum of simple sinusoidal waves of varying frequencies and amplitudes f ( x) = an cos( nω x) n= 0 f(x) is a continuous periodic function over the spatial domain, ω is angular frequency where ω = 2πf, f is the fundamental frequency of the wave, a n is the amplitude for the n th cosine frequency component 14
8 Discrete Cosine Transform 15 Discrete Cosine Transform Consider a single line of pixels across a digital image, which values are [0, 0, 0, 153, 255, 255, 220, 220] F( u) ( ) ( ) F ( ) ( + ) M 1 2 C u 2 1 x u cos r u π f = for 0 r < M u= 0 M 2M 2 where C ( u ) = if u = 0 otherwise C ( u ) = 1 2 is one-dimensional array of coefficients. Each function cos ( 2r+ 1) 2M uπ is called a basis. 16
9 Discrete Cosine Transform ( ) 2r+ 1 uπ Each function cos is called a basis. 2M Can think of each function as a frequency component ( ) The coefficients in F u tell how much each frequency component is weighted in the sum that produces the pixel values think as how much each frequency component contributes to the image 17 Discrete Cosine Transform 18
10 Discrete Cosine Transform 19 Discrete Cosine Transform 20
11 Discrete Cosine Transform 21 Discrete Cosine Transform DCT is stated as follows: ( ) F ( ) f ( ) ( + ) M 1 2C u 2r 1 uπ u = f r cos for 0 u < M r= 0 M 2M 2 where C( u) = if u = 0 otherwise C( u) = 1 2 The equation tells how to transform an image from the spatial domain (grayscale values) to the frequency domain (which gives coefficients by which the frequency components should be multiplied) 22
12 Discrete Cosine Transform 23 Discrete Cosine Transform The first element F(0) ( ) is called the DC component All the other components F(1) through F(M-1) are called AC components 24
13 Discrete Cosine Transform The 2D discrete cosine transform F ( uv) ( ) ( ) ( + ) π ( + ) M 1N 1 2C u C v 2r 1 u 2s 1 vπ, = f( rs, ) cos cos r= 0 s= 0 MN 2 M 2 N 2 where C( δ) = if δ = 0 otherwise C( δ) = 1 2 The 2D inverse discrete cosine transform ( ) ( ) ( + ) π ( + ) M 1N 12C u C v 2r 1 u 2s 1 vπ f ( r, s) = F( u, v) cos cos r= 0 s= 0 MN 2M 2N 2 where C( δ) = if δ = 0 otherwise C( δ) = Discrete Cosine Transform Rather than being applied to a full M N image, the DCT is generally applied to 8 8 pixel subblocks
14 Discrete Cosine Transform Amplitudes of Frequency Components 27 Discrete Cosine Transform 28
15 Aliasing Blurriness and Blockiness Consider Figure 2.1 & 2.2 If the one color changes, the two colors cannot be represented by the sample Imply the image reconstructed from the sample will not be a perfect reproduction of the original scene Mathematically, the spatial frequencies of the original scene will be aliased to lower frequencies in the digital photograph Virtually, when all the colors are averaged to one color, the reconstructed image looks blocky and the edges of objects are jagged 29 Aliasing Blurriness and Blockiness One would need a very high sampling rate to capture a real-world scene with complete fidelity However, the human eye is not going to notice a little lose of detail 30
16 Aliasing Moiré Patterns or Moiré Effect 31 Aliasing Moiré Patterns or Moiré Effect Can result when a digital photograph is taken and when a picture is scanned in to create a digital image 32
17 Aliasing Moiré Patterns or Moiré Effect 33 Aliasing Moiré Patterns or Moiré Effect Occur in digital photography because it is based on discrete samples An alias of the original pattern results if the samples are taken off beat from a detailed pattern in the subject being photographed Sometimes aliasing in digital images manifests itself as small areas of incorrect colors or artificial auras around objects be referred as color aliasing, moiré fringes, false coloration, or phantom colors. 34
18 Aliasing Moiré Patterns or Moiré Effect Many current digital cameras use charge-coupled device (CCD) technology to sense light and thereby color 35 Aliasing Moiré Patterns or Moiré Effect Bayer color filter array, or a Bayer filter There twice as many green sensors as blue or red The interpolation algorithm for deriving the two missing color channels at each photosite is called demosaicing. 36
19 Aliasing Moiré Patterns or Moiré Effect A nearest neighbor algorithm determines a missing color c for a photosite based on the colors of the nearest neighbors that have the color c. G R G B G B B G B G R G G R G B G B Determining R or B from the center G photosite entails an average of two neighboring sites Determining B from the center G photosite entails an average of two neighboring sites 37 Aliasing Jagged Edges Sometime, the term aliasing used to describe the jagged edges along lines or edges that are drawn at an angle across a computer screen Occur during rendering rather than sampling and results from the finite resolution of computer displays 38
20 Aliasing Jagged Edges Anti-aliasing a technique for reducing the jaggedness of lines or edges caused by aliasing 39 Aliasing Jagged Edges Bitmap vs vector graphics 40
21 Color Color Perception and Representation Composed of electromagnetic waves These waves fall upon the color receptors of the eyes the human brain translates the interaction between the waves and the eyes as color perception The colors human see are almost produced d by a combination of wavelengths Possible to represent a color by mean of spectral density graph 41 Color Color Perception and Representation The colors human see are almost produced by a combination of wavelengths 42
22 Color Color Perception and Representation Hue color s dominant wavelength Saturation color purity Luminance the area beneath the curve L= ( d a) e+ ( f e)( c b) S = ( f e )( c b ) L 43 Color C = rr+ gg+ bb RGB Color Model Varying combinations of three primary colors RGB color component (or color channel) The origin i (0, 0, 0) corresponds to black Grayscale values fall along the RGB cube s diagonal from (0, 0, 0) to (1, 1, 1) 44
23 Color CMY Color Model Divide a color into three primaries Using subtractive color creation process The origin of the cube is white (rather than black) The value for each component indicates how much red, green, and blue are subtracted out C = 1 R M = 1 G Y = 1 B 45 Color CMY Color Model Used in professional four-color printed processes by adding a fourth component (pure black) ( C M Y) K = min,, Cnew = C K M new = M K Y = Y K new 46
24 Color 47 Color HSV and HLS Color Models Speak of a color in terms of its hue (essential color), its lightness (or value or luminance), and its saturation (the purity of the color) Geometrically, HSV color space is a distortion of the RGB space into a kind of three-dimensional diamond called a hexacone Hue a position of a point in degrees, from 0 to 360, with red conventionally set at 0 Saturation a function of the color s distance from the central axis. The farther a color is from this axis, the more saturated the color Value axis lies from the black point of the hexacone ranging from 0 for black to 1 for white 48
25 Color HSV and HLS Color Models The distortion of the RGB color space to either HSV or HLS is a non-linear transformation 49 Color 50
26 Color 51 Color Luminance and Chrominance Color Models Capture all the luminance information in one value and put the color (chrominance) information in the other two values; e.g., YIQ model YIQ model is a simple translation of the RGB model More efficient i for television i broadcasting Consolidate all of the black and white information (luminance) in one of the three components and capture all the color information in the other two 52
27 Color Luminance and Chrominance Color Models YIQ model Y R I G = Q B Y is the luminance component, and I and Q are chrominance The coefficients in the matrix are based on primary colors of red, green, and blue that are appropriate for the standard National Television System Committee (NTSC) RGB phosphor p 53 Color Luminance and Chrominance Color Models YUV Originally used in the European PAL analog video standard Based upon luminance and chrominance YCbCr Closely related to the YUV with its chrominance values scaled and shifted Used in JPEG and MPEG compression 54
28 Color CIE XYZ and Color Gamuts The obvious way to generate all possible colors is to combine all possible intensities of red, green, and blue light = 16,777,216 colors There exists colors outside the range of those we can create An experiment called color matching Human subjects are asked to compare pure colors projected onto one side of a screen to composite colors projected beside them The pure colors are created by single wavelength light The amount of the three components are called the tristimulus i values 55 Color CIE XYZ and Color Gamuts Experimentally, no three visible primaries can be linearly combined to produce all colors in the visible spectrum The range of colors that a given monitor can display is called its color gamut There will be colors that t you can represent on your computer monitor but you cannot print, and vice versa 56
29 Color CIE XYZ and Color Gamuts Need of a mathematical model that captures all visible colors CIE XYZ or CIE color model First step in the direction of a standard color model that represents all visible colors Devised in 1931 by the Commission Internationale de l Echlairage 57 Color CIE XYZ and Color Gamuts 58
30 Color CIE XYZ and Color Gamuts The amount of red light energy needed to create the perceived pure spectral red at wavelength λ is a function of the wavelength, given by r(λ), and similarly for green g(λ) and blue b(λ) Let C(λ) be the color the average observer perceives at wavelength λ ( λ ) = ( λ ) + ( λ ) + ( λ ) C r R g G b B R refers to pure spectral red light at a fixed wavelength, and similarly for G and B 59 Color CIE XYZ and Color Gamuts The CIE model is based on the observation that No three visible primary colors that can be combined in positive amounts to create all colors in the visible spectrum Possible to use three virtual primaries to do so These primaries i called X, Y, and Z are theoretical rather than physical entities. Do not correspond to wavelengths of visible light Provide a mathematical way to describe colors that exists in the visible spectrum 60
31 Color CIE XYZ and Color Gamuts X, Y, and Z are chosen so that all three functions remain positive over the wavelengths of the visible spectrum ( λ ) = ( λ) + ( λ) + ( λ) C x X y Y z Z 61 Color CIE XYZ and Color Gamuts 62
32 Color CIE XYZ and Color Gamuts 63 Color CIE XYZ and Color Gamuts 64
33 Color CIE L*a*b, CIE L*U*V, and Perceptual Uniformity The CIE XYZ model has three main advantages Device-independent Provide a way to represent all colors visible to humans The representation is based upon spectrophotometric measurements of color RGB and CMYK color models are not device-independent Different computer monitors or printers can use different values for R, G, and B Their gamuts are not necessarily identical 65 Color CIE L*a*b, CIE L*U*V, and Perceptual Uniformity The CIE XYZ model has a disadvantage that it is not perceptually uniform In a perceptually uniform color space, the distance between two points is directly proportional to the perceived difference between the two colors The Commission International de l Eclairage refined its color model and produced the CIE L*a*b and CIE L*U*V models 66
34 Color Color Management Systems The colors one choose might not be exactly the colors that others see when the picture is placed on the web or printed in hard copy RGB monitor is not identical to the gamut printable in a CMYK color processing system A color management system communicates the assumptions about color spaces, setting for primary colors, and the mapping from color values to physical representations in pixels and ink from one device to another 67 Color Color Management Systems Involve five steps Calibrating your monitor Characterizing your monitor s color profile Creating an individual image s color profile that includes choices for color model and rendering intent Saving the color profile with the image Reproducing the image s color on another device or application program on the basis of the source and destination profiles 68
35 Vector Graphics Geometric Objects in Vector Graphics Drawn object by object File format:.fh Freehand.ai Adobe Illustrator.wmf Windows metafile.eps Encapsulated Postscript Contain the parameters to mathematical ti formulas defining how shapes are drawn 69 Vector Graphics Specifying Curves with Polynomials and Parametric Equations Parametric cubic polynomial functions An n th degree polynomial at + a t + a t at + a n n 1 n 2 n n 1 n where a n 0 and a 0, a 1, a 2,, a n are the coefficients of the polynomial Cubic polynomial (i.e., 3 rd degree polynomials, where the highest power is 3) 70
36 Algorithmic Art and Procedural Modeling Algorithmic art or procedural modeling Creating a digital image by writing a computer program based on some mathematical computation or unique type of algorithm "Octopod" by Mikael Hvidtfeldt Christensen. An example of algorithmic art produced with the software Structure Synth. 71 Algorithmic Art and Procedural Modeling Algorithmic art or procedural modeling Fractal Generation A graphical image characterized by a recursively repeated structure Jon Zander (Digon3)" 72
37 Algorithmic Art and Procedural Modeling 73 Algorithmic Art and Procedural Modeling 74
38 Algorithmic Art and Procedural Modeling The Mandelbrot set 75
Raster Graphics. Overview קורס גרפיקה ממוחשבת 2008 סמסטר ב' What is an image? What is an image? Image Acquisition. Image display 5/19/2008.
Overview Images What is an image? How are images displayed? Color models How do we perceive colors? How can we describe and represent colors? קורס גרפיקה ממוחשבת 2008 סמסטר ב' Raster Graphics 1 חלק מהשקפים
More informationקורס גרפיקה ממוחשבת 2008 סמסטר ב' Raster Graphics 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור
קורס גרפיקה ממוחשבת 2008 סמסטר ב' Raster Graphics 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור Images What is an image? How are images displayed? Color models Overview How
More informationLight. intensity wavelength. Light is electromagnetic waves Laser is light that contains only a narrow spectrum of frequencies
Image formation World, image, eye Light Light is electromagnetic waves Laser is light that contains only a narrow spectrum of frequencies intensity wavelength Visible light is light with wavelength from
More informationColors in Images & Video
LECTURE 8 Colors in Images & Video CS 5513 Multimedia Systems Spring 2009 Imran Ihsan Principal Design Consultant OPUSVII www.opuseven.com Faculty of Engineering & Applied Sciences 1. Light and Spectra
More informationLecture 8. Color Image Processing
Lecture 8. Color Image Processing EL512 Image Processing Dr. Zhu Liu zliu@research.att.com Note: Part of the materials in the slides are from Gonzalez s Digital Image Processing and Onur s lecture slides
More informationColor image processing
Color image processing Color images C1 C2 C3 Each colored pixel corresponds to a vector of three values {C1,C2,C3} The characteristics of the components depend on the chosen colorspace (RGB, YUV, CIELab,..)
More informationCOLOR. and the human response to light
COLOR and the human response to light Contents Introduction: The nature of light The physiology of human vision Color Spaces: Linear Artistic View Standard Distances between colors Color in the TV 2 Amazing
More informationColor images C1 C2 C3
Color imaging Color images C1 C2 C3 Each colored pixel corresponds to a vector of three values {C1,C2,C3} The characteristics of the components depend on the chosen colorspace (RGB, YUV, CIELab,..) Digital
More informationImage and video processing (EBU723U) Colour Images. Dr. Yi-Zhe Song
Image and video processing () Colour Images Dr. Yi-Zhe Song yizhe.song@qmul.ac.uk Today s agenda Colour spaces Colour images PGM/PPM images Today s agenda Colour spaces Colour images PGM/PPM images History
More informationDigital Image Processing Color Models &Processing
Digital Image Processing Color Models &Processing Dr. Hatem Elaydi Electrical Engineering Department Islamic University of Gaza Fall 2015 Nov 16, 2015 Color interpretation Color spectrum vs. electromagnetic
More informationLECTURE 07 COLORS IN IMAGES & VIDEO
MULTIMEDIA TECHNOLOGIES LECTURE 07 COLORS IN IMAGES & VIDEO IMRAN IHSAN ASSISTANT PROFESSOR LIGHT AND SPECTRA Visible light is an electromagnetic wave in the 400nm 700 nm range. The eye is basically similar
More informationSampling Rate = Resolution Quantization Level = Color Depth = Bit Depth = Number of Colors
ITEC2110 FALL 2011 TEST 2 REVIEW Chapters 2-3: Images I. Concepts Graphics A. Bitmaps and Vector Representations Logical vs. Physical Pixels - Images are modeled internally as an array of pixel values
More information12 Color Models and Color Applications. Chapter 12. Color Models and Color Applications. Department of Computer Science and Engineering 12-1
Chapter 12 Color Models and Color Applications 12-1 12.1 Overview Color plays a significant role in achieving realistic computer graphic renderings. This chapter describes the quantitative aspects of color,
More informationComputers and Imaging
Computers and Imaging Telecommunications 1 P. Mathys Two Different Methods Vector or object-oriented graphics. Images are generated by mathematical descriptions of line (vector) segments. Bitmap or raster
More informationColor Science. What light is. Measuring light. CS 4620 Lecture 15. Salient property is the spectral power distribution (SPD)
Color Science CS 4620 Lecture 15 1 2 What light is Measuring light Light is electromagnetic radiation Salient property is the spectral power distribution (SPD) [Lawrence Berkeley Lab / MicroWorlds] exists
More informationIntroduction to Multimedia Computing
COMP 319 Lecture 02 Introduction to Multimedia Computing Fiona Yan Liu Department of Computing The Hong Kong Polytechnic University Learning Outputs of Lecture 01 Introduction to multimedia technology
More informationCOLOR and the human response to light
COLOR and the human response to light Contents Introduction: The nature of light The physiology of human vision Color Spaces: Linear Artistic View Standard Distances between colors Color in the TV 2 How
More informationTo discuss. Color Science Color Models in image. Computer Graphics 2
Color To discuss Color Science Color Models in image Computer Graphics 2 Color Science Light & Spectra Light is an electromagnetic wave It s color is characterized by its wavelength Laser consists of single
More informationColor Image Processing
Color Image Processing Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Color Used heavily in human vision. Visible spectrum for humans is 400 nm (blue) to 700
More informationCS6640 Computational Photography. 6. Color science for digital photography Steve Marschner
CS6640 Computational Photography 6. Color science for digital photography 2012 Steve Marschner 1 What visible light is One octave of the electromagnetic spectrum (380-760nm) NASA/Wikimedia Commons 2 What
More informationWireless Communication
Wireless Communication Systems @CS.NCTU Lecture 4: Color Instructor: Kate Ching-Ju Lin ( 林靖茹 ) Chap. 4 of Fundamentals of Multimedia Some reference from http://media.ee.ntu.edu.tw/courses/dvt/15f/ 1 Outline
More informationIMAGES AND COLOR. N. C. State University. CSC557 Multimedia Computing and Networking. Fall Lecture # 10
IMAGES AND COLOR N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lecture # 10 IMAGES AND COLOR N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lecture
More informationDr. Shahanawaj Ahamad. Dr. S.Ahamad, SWE-423, Unit-06
Dr. Shahanawaj Ahamad 1 Outline: Basic concepts underlying Images Popular Image File formats Human perception of color Various Color Models in use and the idea behind them 2 Pixels -- picture elements
More informationMahdi Amiri. March Sharif University of Technology
Course Presentation Multimedia Systems Color Space Mahdi Amiri March 2014 Sharif University of Technology The wavelength λ of a sinusoidal waveform traveling at constant speed ν is given by Physics of
More informationComputer Graphics Si Lu Fall /27/2016
Computer Graphics Si Lu Fall 2017 09/27/2016 Announcement Class mailing list https://groups.google.com/d/forum/cs447-fall-2016 2 Demo Time The Making of Hallelujah with Lytro Immerge https://vimeo.com/213266879
More informationColor. Used heavily in human vision. Color is a pixel property, making some recognition problems easy
Color Used heavily in human vision Color is a pixel property, making some recognition problems easy Visible spectrum for humans is 400 nm (blue) to 700 nm (red) Machines can see much more; ex. X-rays,
More informationMultimedia Systems Color Space Mahdi Amiri March 2012 Sharif University of Technology
Course Presentation Multimedia Systems Color Space Mahdi Amiri March 2012 Sharif University of Technology Physics of Color Light Light or visible light is the portion of electromagnetic radiation that
More informationColor Image Processing
Color Image Processing Jesus J. Caban Outline Discuss Assignment #1 Project Proposal Color Perception & Analysis 1 Discuss Assignment #1 Project Proposal Due next Monday, Oct 4th Project proposal Submit
More informationIntroduction to computer vision. Image Color Conversion. CIE Chromaticity Diagram and Color Gamut. Color Models
Introduction to computer vision In general, computer vision covers very wide area of issues concerning understanding of images by computers. It may be considered as a part of artificial intelligence and
More informationLecture 3: Grey and Color Image Processing
I22: Digital Image processing Lecture 3: Grey and Color Image Processing Prof. YingLi Tian Sept. 13, 217 Department of Electrical Engineering The City College of New York The City University of New York
More informationVisual Perception. Overview. The Eye. Information Processing by Human Observer
Visual Perception Spring 06 Instructor: K. J. Ray Liu ECE Department, Univ. of Maryland, College Park Overview Last Class Introduction to DIP/DVP applications and examples Image as a function Concepts
More informationAssistant Lecturer Sama S. Samaan
MP3 Not only does MPEG define how video is compressed, but it also defines a standard for compressing audio. This standard can be used to compress the audio portion of a movie (in which case the MPEG standard
More informationColor Science. CS 4620 Lecture 15
Color Science CS 4620 Lecture 15 2013 Steve Marschner 1 [source unknown] 2013 Steve Marschner 2 What light is Light is electromagnetic radiation exists as oscillations of different frequency (or, wavelength)
More informationMultimedia Systems and Technologies
Multimedia Systems and Technologies Faculty of Engineering Master s s degree in Computer Engineering Marco Porta Computer Vision & Multimedia Lab Dipartimento di Ingegneria Industriale e dell Informazione
More informationFor a long time I limited myself to one color as a form of discipline. Pablo Picasso. Color Image Processing
For a long time I limited myself to one color as a form of discipline. Pablo Picasso Color Image Processing 1 Preview Motive - Color is a powerful descriptor that often simplifies object identification
More informationLecture Notes 11 Introduction to Color Imaging
Lecture Notes 11 Introduction to Color Imaging Color filter options Color processing Color interpolation (demozaicing) White balancing Color correction EE 392B: Color Imaging 11-1 Preliminaries Up till
More information6. Graphics MULTIMEDIA & GRAPHICS 10/12/2016 CHAPTER. Graphics covers wide range of pictorial representations. Uses for computer graphics include:
CHAPTER 6. Graphics MULTIMEDIA & GRAPHICS Graphics covers wide range of pictorial representations. Uses for computer graphics include: Buttons Charts Diagrams Animated images 2 1 MULTIMEDIA GRAPHICS Challenges
More informationSampling and Reconstruction. Today: Color Theory. Color Theory COMP575
and COMP575 Today: Finish up Color Color Theory CIE XYZ color space 3 color matching functions: X, Y, Z Y is luminance X and Z are color values WP user acdx Color Theory xyy color space Since Y is luminance,
More informationPerformance Analysis of Color Components in Histogram-Based Image Retrieval
Te-Wei Chiang Department of Accounting Information Systems Chihlee Institute of Technology ctw@mail.chihlee.edu.tw Performance Analysis of s in Histogram-Based Image Retrieval Tienwei Tsai Department of
More informationComputer Graphics. Si Lu. Fall er_graphics.htm 10/02/2015
Computer Graphics Si Lu Fall 2017 http://www.cs.pdx.edu/~lusi/cs447/cs447_547_comput er_graphics.htm 10/02/2015 1 Announcements Free Textbook: Linear Algebra By Jim Hefferon http://joshua.smcvt.edu/linalg.html/
More informationUnderstand brightness, intensity, eye characteristics, and gamma correction, halftone technology, Understand general usage of color
Understand brightness, intensity, eye characteristics, and gamma correction, halftone technology, Understand general usage of color 1 ACHROMATIC LIGHT (Grayscale) Quantity of light physics sense of energy
More informationColor images C1 C2 C3
Color imaging Color images C1 C2 C3 Each colored pixel corresponds to a vector of three values {C1,C2,C3} The characteristics of the components depend on the chosen colorspace (RGB, YUV, CIELab,..) Digital
More informationDIGITAL IMAGING FOUNDATIONS
CHAPTER DIGITAL IMAGING FOUNDATIONS Photography is, and always has been, a blend of art and science. The technology has continually changed and evolved over the centuries but the goal of photographers
More informationColor. Used heavily in human vision. Color is a pixel property, making some recognition problems easy
Color Used heavily in human vision Color is a pixel property, making some recognition problems easy Visible spectrum for humans is 400 nm (blue) to 700 nm (red) Machines can see much more; ex. X-rays,
More informationDigital Image Processing. Lecture # 8 Color Processing
Digital Image Processing Lecture # 8 Color Processing 1 COLOR IMAGE PROCESSING COLOR IMAGE PROCESSING Color Importance Color is an excellent descriptor Suitable for object Identification and Extraction
More informationMULTIMEDIA SYSTEMS
1 Department of Computer Engineering, Faculty of Engineering King Mongkut s Institute of Technology Ladkrabang 01076531 MULTIMEDIA SYSTEMS Pk Pakorn Watanachaturaporn, Wt ht Ph.D. PhD pakorn@live.kmitl.ac.th,
More informationColor Image Processing. Gonzales & Woods: Chapter 6
Color Image Processing Gonzales & Woods: Chapter 6 Objectives What are the most important concepts and terms related to color perception? What are the main color models used to represent and quantify color?
More informationProf. Feng Liu. Fall /04/2018
Prof. Feng Liu Fall 2018 http://www.cs.pdx.edu/~fliu/courses/cs447/ 10/04/2018 1 Last Time Image file formats Color quantization 2 Today Dithering Signal Processing Homework 1 due today in class Homework
More informationIntroduction to Color Science (Cont)
Lecture 24: Introduction to Color Science (Cont) Computer Graphics and Imaging UC Berkeley Empirical Color Matching Experiment Additive Color Matching Experiment Show test light spectrum on left Mix primaries
More informationImage Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester
Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester Lecture 8: Color Image Processing 04.11.2017 Dr. Mohammed Abdel-Megeed Salem Media
More informationColor , , Computational Photography Fall 2018, Lecture 7
Color http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 7 Course announcements Homework 2 is out. - Due September 28 th. - Requires camera and
More informationImaging Process (review)
Color Used heavily in human vision Color is a pixel property, making some recognition problems easy Visible spectrum for humans is 400nm (blue) to 700 nm (red) Machines can see much more; ex. X-rays, infrared,
More informationLecture Color Image Processing. by Shahid Farid
Lecture Color Image Processing by Shahid Farid What is color? Why colors? How we see objects? Photometry, Radiometry and Colorimetry Color measurement Chromaticity diagram Shahid Farid, PUCIT 2 Color or
More informationComputer Graphics. Rendering. Rendering 3D. Images & Color. Scena 3D rendering image. Human Visual System: the retina. Human Visual System
Rendering Rendering 3D Scena 3D rendering image Computer Graphics Università dell Insubria Corso di Laurea in Informatica Anno Accademico 2014/15 Marco Tarini Images & Color M a r c o T a r i n i C o m
More informationDigital Imaging with the Nikon D1X and D100 cameras. A tutorial with Simon Stafford
Digital Imaging with the Nikon D1X and D100 cameras A tutorial with Simon Stafford Contents Fundamental issues of Digital Imaging Camera controls Practical Issues Questions & Answers (hopefully!) Digital
More informationAchim J. Lilienthal Mobile Robotics and Olfaction Lab, AASS, Örebro University
Achim J. Lilienthal Mobile Robotics and Olfaction Lab, Room T1227, Mo, 11-12 o'clock AASS, Örebro University (please drop me an email in advance) achim.lilienthal@oru.se 1 2. General Introduction Schedule
More informationColor , , Computational Photography Fall 2017, Lecture 11
Color http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 11 Course announcements Homework 2 grades have been posted on Canvas. - Mean: 81.6% (HW1:
More informationPhotoshop Domain 2: Identifying Design Elements When Preparing Images
Photoshop Domain 2: Identifying Design Elements When Preparing Images Adobe Creative Suite 5 ACA Certification Preparation: Featuring Dreamweaver, Flash, and Photoshop 1 Objectives Demonstrate knowledge
More informationImage acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor
Image acquisition Digital images are acquired by direct digital acquisition (digital still/video cameras), or scanning material acquired as analog signals (slides, photographs, etc.). In both cases, the
More informationDigital Image Processing. Lecture # 6 Corner Detection & Color Processing
Digital Image Processing Lecture # 6 Corner Detection & Color Processing 1 Corners Corners (interest points) Unlike edges, corners (patches of pixels surrounding the corner) do not necessarily correspond
More informationCS 565 Computer Vision. Nazar Khan PUCIT Lecture 4: Colour
CS 565 Computer Vision Nazar Khan PUCIT Lecture 4: Colour Topics to be covered Motivation for Studying Colour Physical Background Biological Background Technical Colour Spaces Motivation Colour science
More informationHuman Vision, Color and Basic Image Processing
Human Vision, Color and Basic Image Processing Connelly Barnes CS4810 University of Virginia Acknowledgement: slides by Jason Lawrence, Misha Kazhdan, Allison Klein, Tom Funkhouser, Adam Finkelstein and
More informationFig Color spectrum seen by passing white light through a prism.
1. Explain about color fundamentals. Color of an object is determined by the nature of the light reflected from it. When a beam of sunlight passes through a glass prism, the emerging beam of light is not
More informationImages and Colour COSC342. Lecture 2 2 March 2015
Images and Colour COSC342 Lecture 2 2 March 2015 In this Lecture Images and image formats Digital images in the computer Image compression and formats Colour representation Colour perception Colour spaces
More informationImages and Graphics. 4. Images and Graphics - Copyright Denis Hamelin - Ryerson University
Images and Graphics Images and Graphics Graphics and images are non-textual information that can be displayed and printed. Graphics (vector graphics) are an assemblage of lines, curves or circles with
More informationFigure 1: Energy Distributions for light
Lecture 4: Colour The physical description of colour Colour vision is a very complicated biological and psychological phenomenon. It can be described in many different ways, including by physics, by subjective
More informationImage Perception & 2D Images
Image Perception & 2D Images Vision is a matter of perception. Perception is a matter of vision. ES Overview Introduction to ES 2D Graphics in Entertainment Systems Sound, Speech & Music 3D Graphics in
More informationColour Management Workflow
Colour Management Workflow The Eye as a Sensor The eye has three types of receptor called 'cones' that can pick up blue (S), green (M) and red (L) wavelengths. The sensitivity overlaps slightly enabling
More informationColor: Readings: Ch 6: color spaces color histograms color segmentation
Color: Readings: Ch 6: 6.1-6.5 color spaces color histograms color segmentation 1 Some Properties of Color Color is used heavily in human vision. Color is a pixel property, that can make some recognition
More informationIntroduction to Computer Vision CSE 152 Lecture 18
CSE 152 Lecture 18 Announcements Homework 5 is due Sat, Jun 9, 11:59 PM Reading: Chapter 3: Color Electromagnetic Spectrum The appearance of colors Color appearance is strongly affected by (at least):
More informationUnderstanding Color Theory Excerpt from Fundamental Photoshop by Adele Droblas Greenberg and Seth Greenberg
Understanding Color Theory Excerpt from Fundamental Photoshop by Adele Droblas Greenberg and Seth Greenberg Color evokes a mood; it creates contrast and enhances the beauty in an image. It can make a dull
More informationCHAPTER 3 I M A G E S
CHAPTER 3 I M A G E S OBJECTIVES Discuss the various factors that apply to the use of images in multimedia. Describe the capabilities and limitations of bitmap images. Describe the capabilities and limitations
More informationIntroduction to Computer Vision and image processing
Introduction to Computer Vision and image processing 1.1 Overview: Computer Imaging 1.2 Computer Vision 1.3 Image Processing 1.4 Computer Imaging System 1.6 Human Visual Perception 1.7 Image Representation
More informationIntroduction. The Spectral Basis for Color
Introduction Color is an extremely important part of most visualizations. Choosing good colors for your visualizations involves understanding their properties and the perceptual characteristics of human
More informationDigital Image Processing
Digital Image Processing Color Image Processing Christophoros Nikou cnikou@cs.uoi.gr University of Ioannina - Department of Computer Science and Engineering 2 Color Image Processing It is only after years
More informationProf. Feng Liu. Winter /09/2017
Prof. Feng Liu Winter 2017 http://www.cs.pdx.edu/~fliu/courses/cs410/ 01/09/2017 Today Course overview Computer vision Admin. Info Visual Computing at PSU Image representation Color 2 Big Picture: Visual
More informationDigital Images. Back to top-level. Digital Images. Back to top-level Representing Images. Dr. Hayden Kwok-Hay So ENGG st semester, 2010
0.9.4 Back to top-level High Level Digital Images ENGG05 st This week Semester, 00 Dr. Hayden Kwok-Hay So Department of Electrical and Electronic Engineering Low Level Applications Image & Video Processing
More informationVIDEO AND IMAGE PROCESSING USING DSP AND PFGA. Chapter 1: Introduction to Image Processing. Contents
ĐẠI HỌC QUỐC GIA TP.HỒ CHÍ MINH TRƯỜNG ĐẠI HỌC BÁCH KHOA KHOA ĐIỆN-ĐIỆN TỬ BỘ MÔN KỸ THUẬT ĐIỆN TỬ VIDEO AND IMAGE PROCESSING USING DSP AND PFGA Chapter 1: Introduction to Image Processing 1 Contents 1.
More informationColor & Compression. Robin Strand Centre for Image analysis Swedish University of Agricultural Sciences Uppsala University
Color & Compression Robin Strand Centre for Image analysis Swedish University of Agricultural Sciences Uppsala University Outline Color Color spaces Multispectral images Pseudocoloring Color image processing
More informationColor Image Processing EEE 6209 Digital Image Processing. Outline
Outline Color Image Processing Motivation and Color Fundamentals Standard Color Models (RGB/CMYK/HSI) Demosaicing and Color Filtering Pseudo-color and Full-color Image Processing Color Transformation Tone
More informationColor & Graphics. Color & Vision. The complete display system is: We'll talk about: Model Frame Buffer Screen Eye Brain
Color & Graphics The complete display system is: Model Frame Buffer Screen Eye Brain Color & Vision We'll talk about: Light Visions Psychophysics, Colorimetry Color Perceptually based models Hardware models
More informationCS 548: Computer Vision REVIEW: Digital Image Basics. Spring 2016 Dr. Michael J. Reale
CS 548: Computer Vision REVIEW: Digital Image Basics Spring 2016 Dr. Michael J. Reale Human Vision System: Cones and Rods Two types of receptors in eye: Cones Brightness and color Photopic vision = bright-light
More information05 Color. Multimedia Systems. Color and Science
Multimedia Systems 05 Color Color and Science Imran Ihsan Assistant Professor, Department of Computer Science Air University, Islamabad, Pakistan www.imranihsan.com Lectures Adapted From: Digital Multimedia
More informationSilverFast. Colour Management Tutorial. LaserSoft Imaging
SilverFast Colour Management Tutorial LaserSoft Imaging SilverFast Copyright Copyright 1994-2006 SilverFast, LaserSoft Imaging AG, Germany No part of this publication may be reproduced, stored in a retrieval
More informationAnnouncements. Electromagnetic Spectrum. The appearance of colors. Homework 4 is due Tue, Dec 6, 11:59 PM Reading:
Announcements Homework 4 is due Tue, Dec 6, 11:59 PM Reading: Chapter 3: Color CSE 252A Lecture 18 Electromagnetic Spectrum The appearance of colors Color appearance is strongly affected by (at least):
More informationAcquisition and representation of images
Acquisition and representation of images Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Methods for mage Processing academic year 2017 2018 Electromagnetic radiation λ = c ν
More informationIntroduction to Color Theory
Systems & Biomedical Engineering Department SBE 306B: Computer Systems III (Computer Graphics) Dr. Ayman Eldeib Spring 2018 Introduction to With colors you can set a mood, attract attention, or make a
More informationChapter 3 Part 2 Color image processing
Chapter 3 Part 2 Color image processing Motivation Color fundamentals Color models Pseudocolor image processing Full-color image processing: Component-wise Vector-based Recent and current work Spring 2002
More informationAdditive Color Synthesis
Color Systems Defining Colors for Digital Image Processing Various models exist that attempt to describe color numerically. An ideal model should be able to record all theoretically visible colors in the
More informationINSTITUTIONEN FÖR SYSTEMTEKNIK LULEÅ TEKNISKA UNIVERSITET
INSTITUTIONEN FÖR SYSTEMTEKNIK LULEÅ TEKNISKA UNIVERSITET Some color images on this slide Last Lecture 2D filtering frequency domain The magnitude of the 2D DFT gives the amplitudes of the sinusoids and
More informationTerms and Definitions. Scanning
Terms and Definitions Scanning A/D Converter Building block of a scanner. Converts the electric, analog signals to computer-ready, digital signals. Scanners Aliasing The visibility of individual pixels,
More informationimage Scanner, digital camera, media, brushes,
118 Also known as rasterr graphics Record a value for every pixel in the image Often created from an external source Scanner, digital camera, Painting P i programs allow direct creation of images with
More informationIMAGE PROCESSING >COLOR SPACES UTRECHT UNIVERSITY RONALD POPPE
IMAGE PROCESSING >COLOR SPACES UTRECHT UNIVERSITY RONALD POPPE OUTLINE Human visual system Color images Color quantization Colorimetric color spaces HUMAN VISUAL SYSTEM HUMAN VISUAL SYSTEM HUMAN VISUAL
More informationAcquisition and representation of images
Acquisition and representation of images Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Elaborazione delle immagini (Image processing I) academic year 2011 2012 Electromagnetic
More informationColor Management For A Sign Maker. An introduction to a very deep subject.
Color Management For A Sign Maker An introduction to a very deep subject. So Many Terms to remember Color Space Gamut ICC Color Profile RIP Software Preset Files/Media Settings Files Rendering Intents
More informationDigital Technology Group, Inc. Tampa Ft. Lauderdale Carolinas
Digital Technology Group, Inc. Tampa Ft. Lauderdale Carolinas www.dtgweb.com Color Management Defined by Digital Technology Group Absolute Colorimetric One of the four Rendering Intents of the ICC specification.
More informationIntroduction & Colour
Introduction & Colour Eric C. McCreath School of Computer Science The Australian National University ACT 0200 Australia ericm@cs.anu.edu.au Overview 2 Computer Graphics Uses (Chapter 1) Basic Hardware
More informationUnit 8: Color Image Processing
Unit 8: Color Image Processing Colour Fundamentals In 666 Sir Isaac Newton discovered that when a beam of sunlight passes through a glass prism, the emerging beam is split into a spectrum of colours The
More informationColour. Electromagnetic Spectrum (1: visible is very small part 2: not all colours are present in the rainbow!) Colour Lecture!
Colour Lecture! ITNP80: Multimedia 1 Colour What is colour? Human-centric view of colour Computer-centric view of colour Colour models Monitor production of colour Accurate colour reproduction Richardson,
More informationDigital Imaging & Photoshop
Digital Imaging & Photoshop Photoshop Created by Thomas Knoll in 1987, originally called Display Acquired by Adobe in 1988 Released as Photoshop 1.0 for Macintosh in 1990 Released the Creative Suite in
More information