Shujun LI ( 李树钧 ): INF Multimedia Coding. Inputs and Outputs

Size: px
Start display at page:

Download "Shujun LI ( 李树钧 ): INF Multimedia Coding. Inputs and Outputs"

Transcription

1 Lecture/Lab Session 2 Inputs and Outputs May 4, 2009

2 Outline Review Inputs of Encoders: Image/Video Formats Outputs of Decoders: Perceptual Quality Issue MATLAB Exercises Reading and showing images and video sequences Getting familiar with color spaces Examining spatial and temporal redundancies Examining psychovisual redundancies Measuring perceptual quality with PSNR 1

3 Review

4 Image and video coding: A big picture Predictive Coding Input Image/Video Pre- Processing Lossy Coding Lossless Coding Post- Processing Visual Quality Measurement Predictive Coding Encoded Image/Video Decoded Image/Video Post- Lossy Lossless Processing Coding Coding Pre-Processing 3

5 Some terms Luminance = Brightness = Lightness = Intensity = Value Hue: Saturation = Colorfulness = Chroma = Vividness = Purity Chromaticity = Hue + Saturation => It is a 2-tuple. Chromaticity Diagram: a 2-D diagram showing chromaticities Color = Luminance + Chromaticity => So, it is a 3-tuple. Chrominance (Chroma) = Color Difference Color Space: a 3-D space of colors Color Mixing Systems (light) vs. Color Appearance Systems (perception) Gamut: range of colors (in a color space) JND: Just-Noticeable Difference (50% accuracy) 4

6 Shujun LI (李树钧): INF Multimedia Coding HSV/HSL (HSI/HSB) color spaces HSV color space HSL color space 5

7 srgb vs. CIE 1931 XYZ color spaces 6

8 Inputs of Encoders: Image/Video Formats

9 Color conversion in image/video encoding Pre-Processing Input Image/Video Color Space A/D RGB R G B Conversion Y UV/ Y P b P r Conversion Y C b C r Chroma Subsampling Encoded Image/Video Other Parts of Encoding Process 8

10 Color conversion: R G B GB => Y C b C r R,G,B [0,1],,, [, k r r, k b (0,1), (, k r+k b<1 Y =k r *R +(1-k r -k b )*G +k b *B [0,1] P b =0.5*(B -Y )/(1-k Y k b ) [-0.5,0.5] P r =0.5*(R -Y )/(1-k r ) [-0.5,0.5] Values of k b and k r can be different! ITU-T BT.601 (SDTV): k r =0.229, k b =0.114 ITU-T T BT.709 (HDTV): k r =0.2126, k b = ANSI/SMPTE 240M-1995 (HDTV): k r =0.212, k b =0.087 Y P YP b P r => Y C b C r (taking MPEG-2asanexample) an Y =219*Y +16 [16,235] C b=224*p b +128 [16,240] C r =224*P r +128 [16,240] 9

11 Chroma subsampling formats A 4X2 Image P(1,1) P(1,2) P(1,3) P(1,4) P(2,1) P(2,2) 2) P(2,3) P(2,4) Y Sampling C Y C b C b Sampling C r Sampling r Locations Locations Locations 4:4: ALL ALL ALL 4:2: ALL P(1,1) P(1,3) P(2,1) P(2,3) P(1,1) P(1,3) P(2,1) P(2,3) 4:2: ALL P(1,1) P(1,3) P(1,1) P(1,3) 4:1: ALL P(1,1) P(2,1) P(1,1) P(2,1) 10

12 Progressive vs. Interlacing Progressive mode: each row of a frame is scanned one by one Interlacing mode: a frame is divided into two fields odd rows are scanned first and even rows later. Benefit: bandwidth saving close to ½ Hz interlacing HDTV: 1920/ = G bits/second Hz progressive HDTV: = G bits/second Problem: 11

13 Interlacing mode: Frames vs. Fields A frame may be divided into two fields Top filed + Bottom field 12

14 Interlacing mode: Frames vs. Fields A frame may be divided into two fields Top filed + Bottom field 13

15 Shujun LI (李树钧): INF Multimedia Coding Interlacing mode: Frames vs. Fields z A frame may be divided into two fields Top filed + Bottom field 14

16 Digital image formats No standard spatial resolutions For research purpose: 2 n 2 n, such as , , , Uncompressed dimages BMP (*.bmp): fileheader + infoheader + [palette] + data (row by row, bottom-up) Netpbm formats: PBM/PGM/PPM (*.pbm/*.pgm/*.ppm) Losslessly compressed images TIFF = Tagged Image File Format (*.tiff/*.tif) PNG = Portable Network Graphics (*.png) GIF = Graphics Interchange Format (*.gif) up to 256 colors Lossily compressed images JPEG (*.jpg), JPEG2000 (*.jp2/*.j2k) j2k) 15

17 Digital video formats CIF = Common Intermediate Format (since H.261) CIF (Full CIF = FCIF) = QCIF (Quarter CIF) = SQCIF (Sub Quarter CIF) = CIF = 4 CIF = CIF = 4 4CIF = SIF = Source Input Format (since MPEG-1) 625/50 (TV: PAL/SECAM) = / /59.94 (TV: NTSC) = / Sub-SIF (Computers) = or

18 YUV video file format (.yuv/.cif/.qcif/.sif/ ) Planar formats YUV = YV12 = I420 = IYUV (4:2:0) YV16 (4:2:2) Packed formats UYVY = UYNV = Y422 (4:2:2) YUY2 = YUNV = V422 = YUYV (4:2:2) More info available at 17

19 YUV4MPEG2 format (.y4m) File Header File signature: YUV4MPEG2 Parameters Frames Width, height ht and frame rate: Wxxx Hyyy Fa:b Interlacing: Ip (progressive), It (top field first), Ib (bottom field first), Im (mixed mode, detailed in frame headers) Aspect ratio: Aa:b Color space (Chroma format): C4xx... Comment: X. Frame Header FRAME + a number of parameters (optional) + 0x0A Frame (YUV planar format) More information is available at 18

20 Multimedia container/wrapper formats AVI = Audio Video Interleave (*.avi) FLV = Flash Video (*.flv) ASF = Advanced Systems Format (*.asf) MPEG-TS (Transport Stream) & MPEG-PS (Program Stream) (*.mpg/*.ts/*.ps) MP4 = MPEG-4 Part 14 (*.mp4) => 3GP (*.3gp/*.3g2) MOV (Quicktime) (*.mov) RealMedia (*.rm/ rm/*.rmvb) 19

21 Shujun LI (李树钧): INF Multimedia Coding Outputs of Decoders: Perceptual Quality Issue

22 Image and video coding: Quality issue Quality Mt Meterics Input image/video Decoded image/video Visual Quality Measurement Encoder Decoder Encoded image/video 21

23 Visual quality measurement: Subjective DSIS (Double Stimulus Impairment Scale) DSCQS (Double Stimulus Continuous Quality Scale) SSCQE (Single Stimulus Continuous Quality Evaluation) Some measurement methods have been standardized: ITU-R BT.500, ITU-R BT.710, ITU-T P

24 Visual quality measurement: Objective Two images: the original one f(x,y) and the decoded one f (x,y) MSE = Mean Squared Error 1 MN X (f 0 (x, y) f(x, y)) 2 x,y SNR = Signal-to-Noise Ratio à P! x,y (f 0 (x, y)) 2 10 log 10 P x,y (f 0 (x, y) f(x, y)) 2 PSNR = Peak Signal-to-Noise Ratio à 10 log 10 L 2 P x,y (f 0 (x,, y) f(x, y)) 2! 23

25 Shujun LI (李树钧): INF Multimedia Coding Visual quality measurement: Objective The original image PSNR=32.7 db PSNR=37.5 db Research Question: PSNR is not g good enough g to perfectly p y reflect visual quality, more advanced metrics considering HVS are wanted. Some objective metrics are being standardized by the ITU-T. 24

26 Visual quality measurement: Objective SSIM = Structural Similarity Index SSIM = (2E(f(x, y)f 0 (x, y)) + C 1 )(2σ f,f 0 + C 2 ) (E(f(x, y)) 2 + E(f 0 (x, y)) 2 + C 1 ) ³σ 2f + σ2f + C 2 0 VQM = Video Quality Metric MPQM = Moving Pictures Quality Metric NQM = Noise Quality Measure f f 25

27 MATLAB Exercises

28 Reading and Showing Image/Video Read an image. f=imread( Images/lena_color.bmp ); Show an image. imshow(f); Read a frame from a YUV video. f=yuvread( Video/news.qcif,1); Show a video frame. imshow(ycbcr2rgb(yuv4xx_444(f))); Play back a YUV video. yuvplay( Video/news.qcif, All ); 27

29 Getting familiar with color spaces Show YUV planes of a video. f=yuvread( test.cif',1); subplot(2,2,1:2); imshow(f.y); subplot(2,2,3); imshow(f.u); subplot(2,2,4); imshow(f.v); Try to add pseudo-colors to U- and V-planes. Tip: assume Y=0.5 and another chroma channel is 0. Try to read a RGB image and convert it to YUV color space. 28

30 YUV video player => Y4M video player Read the code of YUV video player. Two files: YUVplayer.m and YUVplayer.fig. Run guide YUVplayer.fig to open the second file. Read MATLAB help documents to learn how to design GUI with MATLAB. Try to implement a Y4M video player. This can be a take-home assignment. 29

Compression and Image Formats

Compression and Image Formats Compression Compression and Image Formats Reduce amount of data used to represent an image/video Bit rate and quality requirements Necessary to facilitate transmission and storage Required quality is application

More information

Digital Asset Management 2. Introduction to Digital Media Format

Digital Asset Management 2. Introduction to Digital Media Format Digital Asset Management 2. Introduction to Digital Media Format 2010-09-09 Content content = essence + metadata 2 Digital media data types Table. File format used in Macromedia Director File import File

More information

Multimedia Systems Color Space Mahdi Amiri March 2012 Sharif University of Technology

Multimedia 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 information

LECTURE 03 BITMAP IMAGE FORMATS

LECTURE 03 BITMAP IMAGE FORMATS MULTIMEDIA TECHNOLOGIES LECTURE 03 BITMAP IMAGE FORMATS IMRAN IHSAN ASSISTANT PROFESSOR IMAGE FORMATS To store an image, the image is represented in a two dimensional matrix of pixels. Information about

More information

Mahdi Amiri. March Sharif University of Technology

Mahdi 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 information

15110 Principles of Computing, Carnegie Mellon University

15110 Principles of Computing, Carnegie Mellon University 1 Overview Human sensory systems and digital representations Digitizing images Digitizing sounds Video 2 HUMAN SENSORY SYSTEMS 3 Human limitations Range only certain pitches and loudnesses can be heard

More information

15110 Principles of Computing, Carnegie Mellon University

15110 Principles of Computing, Carnegie Mellon University 1 Last Time Data Compression Information and redundancy Huffman Codes ALOHA Fixed Width: 0001 0110 1001 0011 0001 20 bits Huffman Code: 10 0000 010 0001 10 15 bits 2 Overview Human sensory systems and

More information

Ch. 3: Image Compression Multimedia Systems

Ch. 3: Image Compression Multimedia Systems 4/24/213 Ch. 3: Image Compression Multimedia Systems Prof. Ben Lee (modified by Prof. Nguyen) Oregon State University School of Electrical Engineering and Computer Science Outline Introduction JPEG Standard

More information

Anti aliasing and Graphics Formats

Anti aliasing and Graphics Formats Anti aliasing and Graphics Formats Eric C. McCreath School of Computer Science The Australian National University ACT 0200 Australia ericm@cs.anu.edu.au Overview 2 Nyquist sampling frequency supersampling

More information

Assistant Lecturer Sama S. Samaan

Assistant 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 information

ORIGINAL ARTICLE A COMPARATIVE STUDY OF QUALITY ANALYSIS ON VARIOUS IMAGE FORMATS

ORIGINAL ARTICLE A COMPARATIVE STUDY OF QUALITY ANALYSIS ON VARIOUS IMAGE FORMATS ORIGINAL ARTICLE A COMPARATIVE STUDY OF QUALITY ANALYSIS ON VARIOUS IMAGE FORMATS 1 M.S.L.RATNAVATHI, 1 SYEDSHAMEEM, 2 P. KALEE PRASAD, 1 D. VENKATARATNAM 1 Department of ECE, K L University, Guntur 2

More information

Computer and Machine Vision

Computer and Machine Vision Computer and Machine Vision Lecture Week 7 Part-2 (Exam #1 Review) February 26, 2014 Sam Siewert Outline of Week 7 Basic Convolution Transform Speed-Up Concepts for Computer Vision Hough Linear Transform

More information

21 CP Clarify Photometric Interpretation after decompression of compressed Transfer Syntaxes Page 1

21 CP Clarify Photometric Interpretation after decompression of compressed Transfer Syntaxes Page 1 21 CP-1565 - Clarify Photometric Interpretation after decompression of compressed Transfer Syntaxes Page 1 1 Status May 2016 Packet 2 Date of Last Update 2016/03/18 3 Person Assigned David Clunie 4 mailto:dclunie@dclunie.com

More information

Computers and Imaging

Computers 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 information

IMAGES 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 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 information

CS 262 Lecture 01: Digital Images and Video. John Magee Some material copyright Jones and Bartlett

CS 262 Lecture 01: Digital Images and Video. John Magee Some material copyright Jones and Bartlett CS 262 Lecture 01: Digital Images and Video John Magee Some material copyright Jones and Bartlett 1 Overview/Questions What is digital information? What is color? How do pictures get encoded into binary

More information

Bitmap Image Formats

Bitmap Image Formats LECTURE 5 Bitmap Image Formats CS 5513 Multimedia Systems Spring 2009 Imran Ihsan Principal Design Consultant OPUSVII www.opuseven.com Faculty of Engineering & Applied Sciences 1. Image Formats To store

More information

Multimedia. Graphics and Image Data Representations (Part 2)

Multimedia. Graphics and Image Data Representations (Part 2) Course Code 005636 (Fall 2017) Multimedia Graphics and Image Data Representations (Part 2) Prof. S. M. Riazul Islam, Dept. of Computer Engineering, Sejong University, Korea E-mail: riaz@sejong.ac.kr Outline

More information

Imaging Process (review)

Imaging 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 information

Wireless Communication

Wireless 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 information

Images and Colour COSC342. Lecture 2 2 March 2015

Images 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 information

Color & 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 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 information

Digital Imaging & Photoshop

Digital 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

Computer Graphics. Si Lu. Fall er_graphics.htm 10/02/2015

Computer 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 information

Recommendation ITU-R BT.1866 (03/2010)

Recommendation ITU-R BT.1866 (03/2010) Recommendation ITU-R BT.1866 (03/2010) Objective perceptual video quality measurement techniques for broadcasting applications using low definition television in the presence of a full reference signal

More information

CHAPTER 3 I M A G E S

CHAPTER 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 information

Introduction to Multimedia Computing

Introduction 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 information

Sampling and Reconstruction. Today: Color Theory. Color Theory COMP575

Sampling 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 information

Camera, video production. TNGD10 - Moving media

Camera, video production. TNGD10 - Moving media Camera, video production TNGD10 - Moving media Parallel vs serial information Film and projector is parallel information But, to distribute film you need serial information You achieve this by dividing

More information

Introduction to Color Theory

Introduction 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 information

Digital Images: A Technical Introduction

Digital Images: A Technical Introduction Digital Images: A Technical Introduction Images comprise a significant portion of a multimedia application This is an introduction to what is under the technical hood that drives digital images particularly

More information

Image Perception & 2D Images

Image 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 information

Image and video processing (EBU723U) Colour Images. Dr. Yi-Zhe Song

Image 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 information

LIST OF SYMBOLS AND ABBREVIATIONS

LIST OF SYMBOLS AND ABBREVIATIONS viii TABLE OF CONTENTS CHAPTER NO. TITLE PAGE NO. ABSTRACT LIST OF TABLES LIST OF FIGURES LIST OF SYMBOLS AND ABBREVIATIONS iii xii xiii xvi 1 INTRODUCTION 1 1.1 GENERAL 1 1.2 IMAGE REPRESENTATIONS 2 1.3

More information

Visual Perception. Overview. The Eye. Information Processing by Human Observer

Visual 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 information

UNIT 7C Data Representation: Images and Sound

UNIT 7C Data Representation: Images and Sound UNIT 7C Data Representation: Images and Sound 1 Pixels An image is stored in a computer as a sequence of pixels, picture elements. 2 1 Resolution The resolution of an image is the number of pixels used

More information

Light. intensity wavelength. Light is electromagnetic waves Laser is light that contains only a narrow spectrum of frequencies

Light. 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 information

Digital Image Processing Color Models &Processing

Digital 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 information

Computer Graphics. Rendering. Rendering 3D. Images & Color. Scena 3D rendering image. Human Visual System: the retina. Human Visual System

Computer 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 information

OFFSET AND NOISE COMPENSATION

OFFSET AND NOISE COMPENSATION OFFSET AND NOISE COMPENSATION AO 10V 8.1 Offset and fixed pattern noise reduction Offset variation - shading AO 10V 8.2 Row Noise AO 10V 8.3 Offset compensation Global offset calibration Dark level is

More information

Information Hiding: Steganography & Steganalysis

Information Hiding: Steganography & Steganalysis Information Hiding: Steganography & Steganalysis 1 Steganography ( covered writing ) From Herodotus to Thatcher. Messages should be undetectable. Messages concealed in media files. Perceptually insignificant

More information

Introduction. Prof. Lina Karam School of Electrical, Computer, & Energy Engineering Arizona State University

Introduction. Prof. Lina Karam School of Electrical, Computer, & Energy Engineering Arizona State University EEE 508 - Digital Image & Video Processing and Compression http://lina.faculty.asu.edu/eee508/ Introduction Prof. Lina Karam School of Electrical, Computer, & Energy Engineering Arizona State University

More information

Color image processing

Color 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 information

Images and Graphics. 4. Images and Graphics - Copyright Denis Hamelin - Ryerson University

Images 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 information

B.Digital graphics. Color Models. Image Data. RGB (the additive color model) CYMK (the subtractive color model)

B.Digital graphics. Color Models. Image Data. RGB (the additive color model) CYMK (the subtractive color model) Image Data Color Models RGB (the additive color model) CYMK (the subtractive color model) Pixel Data Color Depth Every pixel is assigned to one specific color. The amount of data stored for every pixel,

More information

LECTURE 07 COLORS IN IMAGES & VIDEO

LECTURE 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 information

Information representation

Information representation 2Unit Chapter 11 1 Information representation Revision objectives By the end of the chapter you should be able to: show understanding of the basis of different number systems; use the binary, denary and

More information

Subjective evaluation of image color damage based on JPEG compression

Subjective evaluation of image color damage based on JPEG compression 2014 Fourth International Conference on Communication Systems and Network Technologies Subjective evaluation of image color damage based on JPEG compression Xiaoqiang He Information Engineering School

More information

Byte = More common: 8 bits = 1 byte Abbreviation:

Byte = More common: 8 bits = 1 byte Abbreviation: Text, Images, Video and Sound ASCII-7 In the early days, a was used, with of 0 s and 1 s, enough for a typical keyboard. The standard was developed by (American Standard Code for Information Interchange)

More information

IMAGE PROCESSING >COLOR SPACES UTRECHT UNIVERSITY RONALD POPPE

IMAGE 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 information

35 CP JPEG-LS Planar Configuration constraints conflict with WSI, US, VL, Enhanced Color MR and Page 1 36 compressed RGB images

35 CP JPEG-LS Planar Configuration constraints conflict with WSI, US, VL, Enhanced Color MR and Page 1 36 compressed RGB images 35 CP-1843 - JPEG-LS Planar Configuration constraints conflict with WSI, US, VL, Enhanced Color MR and Page 1 36 compressed RGB images 1 Status Jan 2019 Voting Packet 2 Date of Last Update 2018/11/12 3

More information

Colors in Images & Video

Colors 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 information

Lecture 8. Color Image Processing

Lecture 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 information

Color images C1 C2 C3

Color 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 information

Raster (Bitmap) Graphic File Formats & Standards

Raster (Bitmap) Graphic File Formats & Standards Raster (Bitmap) Graphic File Formats & Standards Contents Raster (Bitmap) Images Digital Or Printed Images Resolution Colour Depth Alpha Channel Palettes Antialiasing Compression Colour Models RGB Colour

More information

Image is a spatial representation of an object or a scene. (image of a person, place, object)

Image is a spatial representation of an object or a scene. (image of a person, place, object) Graphics & Images Table of Content 1. Introduction 2. Types of graphics 3. Resolution 4. Memory/Storage requirement 5. Types of images 6. Image colour schemes 7. Colour dithering 8. Image processing 9.

More information

Indexed Color. A browser may support only a certain number of specific colors, creating a palette from which to choose

Indexed Color. A browser may support only a certain number of specific colors, creating a palette from which to choose Indexed Color A browser may support only a certain number of specific colors, creating a palette from which to choose Figure 3.11 The Netscape color palette 1 QUIZ How many bits are needed to represent

More information

Raster Image File Formats

Raster Image File Formats Raster Image File Formats 1995-2016 Josef Pelikán & Alexander Wilkie CGG MFF UK Praha pepca@cgg.mff.cuni.cz http://cgg.mff.cuni.cz/~pepca/ 1 / 35 Raster Image Capture Camera Area sensor (CCD, CMOS) Colours:

More information

CGT 511. Image. Image. Digital Image. 2D intensity light function z=f(x,y) defined over a square 0 x,y 1. the value of z can be:

CGT 511. Image. Image. Digital Image. 2D intensity light function z=f(x,y) defined over a square 0 x,y 1. the value of z can be: Image CGT 511 Computer Images Bedřich Beneš, Ph.D. Purdue University Department of Computer Graphics Technology Is continuous 2D image function 2D intensity light function z=f(x,y) defined over a square

More information

Lecture - 3. by Shahid Farid

Lecture - 3. by Shahid Farid Lecture - 3 by Shahid Farid Image Digitization Raster versus vector images Progressive versus interlaced display Popular image file formats Why so many formats? Shahid Farid, PUCIT 2 To create a digital

More information

Color and Perception. CS535 Fall Daniel G. Aliaga Department of Computer Science Purdue University

Color and Perception. CS535 Fall Daniel G. Aliaga Department of Computer Science Purdue University Color and Perception CS535 Fall 2014 Daniel G. Aliaga Department of Computer Science Purdue University Elements of Color Perception 2 Elements of Color Physics: Illumination Electromagnetic spectra; approx.

More information

CGT 211 Sampling and File Formats

CGT 211 Sampling and File Formats CGT 211 Sampling and File Formats The Physics of What We Do 2 types of waves - electromagnetic and pressure Analog frequency variations, infinite defines color, brightness, pitch, volume Digital Data Binary

More information

3.1 Graphics/Image age Data Types. 3.2 Popular File Formats

3.1 Graphics/Image age Data Types. 3.2 Popular File Formats Chapter 3 Graphics and Image Data Representations 3.1 Graphics/Image Data Types 3.2 Popular File Formats 3.1 Graphics/Image age Data Types The number of file formats used in multimedia continues to proliferate.

More information

Announcements. Electromagnetic Spectrum. The appearance of colors. Homework 4 is due Tue, Dec 6, 11:59 PM Reading:

Announcements. 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 information

Introduction to Computer Vision CSE 152 Lecture 18

Introduction 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 information

UNIT 7C Data Representation: Images and Sound Principles of Computing, Carnegie Mellon University CORTINA/GUNA

UNIT 7C Data Representation: Images and Sound Principles of Computing, Carnegie Mellon University CORTINA/GUNA UNIT 7C Data Representation: Images and Sound Carnegie Mellon University CORTINA/GUNA 1 Announcements Pa6 is available now 2 Pixels An image is stored in a computer as a sequence of pixels, picture elements.

More information

Color: Readings: Ch 6: color spaces color histograms color segmentation

Color: 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 information

On the WEB. Digital Image Processing ECE 178. B. S. MANJUNATH RM 3157 ENGR I Tel:

On the WEB. Digital Image Processing ECE 178. B. S. MANJUNATH RM 3157 ENGR I Tel: Digital Image Processing ECE 178 B. S. MANJUNATH RM 3157 ENGR I Tel:893-7112 manj@ece.ucsb.edu http://vision.ece.ucsb.edu Introduction 1 On the WEB For course information: http://www.ece.ucsb.edu/~manj/ece178

More information

Images with (a) coding redundancy; (b) spatial redundancy; (c) irrelevant information

Images with (a) coding redundancy; (b) spatial redundancy; (c) irrelevant information Images with (a) coding redundancy; (b) spatial redundancy; (c) irrelevant information 1992 2008 R. C. Gonzalez & R. E. Woods For the image in Fig. 8.1(a): 1992 2008 R. C. Gonzalez & R. E. Woods Measuring

More information

Raster Graphics. Overview קורס גרפיקה ממוחשבת 2008 סמסטר ב' What is an image? What is an image? Image Acquisition. Image display 5/19/2008.

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 information

קורס 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 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 information

13 Compressed RGB components (rather than YBR) really are used by some WSI vendors in order to avoid the loss in conversion of 14 color spaces.

13 Compressed RGB components (rather than YBR) really are used by some WSI vendors in order to avoid the loss in conversion of 14 color spaces. 18 CP-1841 - Allow compressed RGB for WSI Page 1 1 Status Jan 2019 Voting Packet 2 Date of Last Update 2018/11/12 3 Person Assigned David Clunie 4 mailto:dclunie@dclunie.com 5 Submitter Name Aaron Stearrett

More information

COLOR and the human response to light

COLOR 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 information

Dr. Shahanawaj Ahamad. Dr. S.Ahamad, SWE-423, Unit-06

Dr. 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 information

Adding some light to computing. Lawrence Snyder University of Washington, Seattle

Adding some light to computing. Lawrence Snyder University of Washington, Seattle Adding some light to computing. Lawrence Snyder University of Washington, Seattle Lawrence Snyder 2004 Recall that the screen (and other video displays) use red- green- blue lights, arranged in an array

More information

UNIT 7B Data Representa1on: Images and Sound. Pixels. An image is stored in a computer as a sequence of pixels, picture elements.

UNIT 7B Data Representa1on: Images and Sound. Pixels. An image is stored in a computer as a sequence of pixels, picture elements. UNIT 7B Data Representa1on: Images and Sound 1 Pixels An image is stored in a computer as a sequence of pixels, picture elements. 2 1 Resolu1on The resolu1on of an image is the number of pixels used to

More information

Reading instructions: Chapter 6

Reading instructions: Chapter 6 Lecture 8 in Computerized Image Analysis Digital Color Processing Hamid Sarve hamid@cb.uu.se Reading instructions: Chapter 6 Electromagnetic Radiation Visible light (for humans) is electromagnetic radiation

More information

Why Visual Quality Assessment?

Why Visual Quality Assessment? Why Visual Quality Assessment? Sample image-and video-based applications Entertainment Communications Medical imaging Security Monitoring Visual sensing and control Art Why Visual Quality Assessment? What

More information

Experimental Images Analysis with Linear Change Positive and Negative Degree of Brightness

Experimental Images Analysis with Linear Change Positive and Negative Degree of Brightness Experimental Images Analysis with Linear Change Positive and Negative Degree of Brightness 1 RATKO IVKOVIC, BRANIMIR JAKSIC, 3 PETAR SPALEVIC, 4 LJUBOMIR LAZIC, 5 MILE PETROVIC, 1,,3,5 Department of Electronic

More information

Specific structure or arrangement of data code stored as a computer file.

Specific structure or arrangement of data code stored as a computer file. FILE FORMAT Specific structure or arrangement of data code stored as a computer file. A file format tells the computer how to display, print, process, and save the data. It is dictated by the application

More information

Color. 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 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 information

xyy L*a*b* L*u*v* RGB

xyy L*a*b* L*u*v* RGB The RGB code Part 2: Cracking the RGB code (from XYZ to RGB, and other codes ) In the first part of his quest to crack the RGB code, our hero saw how to get XYZ numbers by combining a Standard Observer

More information

Introduction to Computer Vision and image processing

Introduction 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 information

Compression. Encryption. Decryption. Decompression. Presentation of Information to client site

Compression. Encryption. Decryption. Decompression. Presentation of Information to client site DOCUMENT Anup Basu Audio Image Video Data Graphics Objectives Compression Encryption Network Communications Decryption Decompression Client site Presentation of Information to client site Multimedia -

More information

Chapter 8. Representing Multimedia Digitally

Chapter 8. Representing Multimedia Digitally Chapter 8 Representing Multimedia Digitally Learning Objectives Explain how RGB color is represented in bytes Explain the difference between bits and binary numbers Change an RGB color by binary addition

More information

Practical Content-Adaptive Subsampling for Image and Video Compression

Practical Content-Adaptive Subsampling for Image and Video Compression Practical Content-Adaptive Subsampling for Image and Video Compression Alexander Wong Department of Electrical and Computer Eng. University of Waterloo Waterloo, Ontario, Canada, N2L 3G1 a28wong@engmail.uwaterloo.ca

More information

CHAPTER 2 - DIGITAL DATA REPRESENTATION AND NUMBERING SYSTEMS

CHAPTER 2 - DIGITAL DATA REPRESENTATION AND NUMBERING SYSTEMS CHAPTER 2 - DIGITAL DATA REPRESENTATION AND NUMBERING SYSTEMS INTRODUCTION Digital computers use sequences of binary digits (bits) to represent numbers, letters, special symbols, music, pictures, and videos.

More information

INTRODUCTION TO COMPUTER GRAPHICS

INTRODUCTION TO COMPUTER GRAPHICS INTRODUCTION TO COMPUTER GRAPHICS ITC 31012: GRAPHICAL DESIGN APPLICATIONS AJM HASMY hasmie@gmail.com WHAT CAN PS DO? - PHOTOSHOPPING CREATING IMAGE Custom icons, buttons, lines, balls or text art web

More information

Color. 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 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 information

Image Processing Computer Graphics I Lecture 20. Display Color Models Filters Dithering Image Compression

Image Processing Computer Graphics I Lecture 20. Display Color Models Filters Dithering Image Compression 15-462 Computer Graphics I Lecture 2 Image Processing April 18, 22 Frank Pfenning Carnegie Mellon University http://www.cs.cmu.edu/~fp/courses/graphics/ Display Color Models Filters Dithering Image Compression

More information

Prof. Feng Liu. Fall /02/2018

Prof. Feng Liu. Fall /02/2018 Prof. Feng Liu Fall 2018 http://www.cs.pdx.edu/~fliu/courses/cs447/ 10/02/2018 1 Announcements Free Textbook: Linear Algebra By Jim Hefferon http://joshua.smcvt.edu/linalg.html/ Homework 1 due in class

More information

The next table shows the suitability of each format to particular applications.

The next table shows the suitability of each format to particular applications. What are suitable file formats to use? The four most common file formats used are: TIF - Tagged Image File Format, uncompressed and compressed formats PNG - Portable Network Graphics, standardized compression

More information

Developing Multimedia Assets using Fireworks and Flash

Developing Multimedia Assets using Fireworks and Flash HO-2: IMAGE FORMATS Introduction As you will already have observed from browsing the web, it is possible to add a wide range of graphics to web pages, including: logos, animations, still photographs, roll-over

More information

Coding of Still Pictures

Coding of Still Pictures ISO/IEC JTC 1/SC 29/WG1 N 80024 80 th Meeting Berlin, Germany, 7-13 July 2018 ISO/IEC JTC 1/SC 29/WG 1 (& ITU-T SG16) Coding of Still Pictures JBIG Joint Bi-level Image Experts Group JPEG Joint Photographic

More information

Introduction to Computer Vision

Introduction to Computer Vision Introduction to Computer Vision CS / ECE 181B Thursday, April 1, 2004 Course Details HW #0 and HW #1 are available. Course web site http://www.ece.ucsb.edu/~manj/cs181b Syllabus, schedule, lecture notes,

More information

JPEG Encoder Using Digital Image Processing

JPEG Encoder Using Digital Image Processing International Journal of Emerging Trends in Science and Technology JPEG Encoder Using Digital Image Processing Author M. Divya M.Tech (ECE) / JNTU Ananthapur/Andhra Pradesh DOI: http://dx.doi.org/10.18535/ijetst/v2i10.08

More information

ITP 140 Mobile App Technologies. Images

ITP 140 Mobile App Technologies. Images ITP 140 Mobile App Technologies Images Images All digital images are rectangles! Each image has a width and height 2 Terms Pixel A picture element Screen size In inches Resolution A width and height DPI

More information

ENEE408G Multimedia Signal Processing

ENEE408G Multimedia Signal Processing ENEE48G Multimedia Signal Processing Design Project on Image Processing and Digital Photography Goals:. Understand the fundamentals of digital image processing.. Learn how to enhance image quality and

More information

1 Li & Drew c Prentice Hall Li & Drew c Prentice Hall 2003

1 Li & Drew c Prentice Hall Li & Drew c Prentice Hall 2003 Chapter 3 Graphics and Image Data Representations 3.1 Graphics/Image Data Types 3.2 Popular File Formats 3.3 Further Exploration 3.1 Graphics/Image Data Types The number of file formats used in multimedia

More information

Image Processing: An Overview

Image Processing: An Overview Image Processing: An Overview Sebastiano Battiato, Ph.D. battiato@dmi.unict.it Program Image Representation & Color Spaces Image files format (Compressed/Not compressed) Bayer Pattern & Color Interpolation

More information