Picture and Video Coding and Processing Lecture 1: Class Overview

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Course Basics. ECE 332:529 (Image and Video Processing) is a graduate course that expands upon essential computerized sign preparing (e.g. the Rutgers DSF course).Prerequisites:An early on graduate course in sign preparing (DSF)Stochastic Signals and Systems is very recommendedNotes: The course portrayal website page states 332:550 is an essential. Nobody has seen 550 in ages

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Picture (and Video) Coding and Processing Lecture 1: Class Overview Wade Trappe

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Course Basics ECE 332:529 (Image and Video Processing) is a graduate course that expands upon fundamental computerized flag preparing (e.g. the Rutgers DSF course). Requirements: An early on graduate course in flag preparing (DSF) Stochastic Signals and Systems is exceedingly prescribed Notes: The course depiction website page states 332:550 is an essential. Nobody has seen 550 in ages… The course portrayal likewise says that 332:535 (multidimensional flag handling) is prescribed. This is not valid. 535 is once in a while offered and will be contained in this class.

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Course Basics, pg. 2 A center graduate DSP educational programs comprises of: A propelled course on advanced flag channels A class on multirate signs and frameworks A class on picture and video flag handling A class on versatile and ideal flag preparing At Rutgers, the DSF course is required The other 3 classes are fundamental to having the capacity to state you "know flag handling" when you graduate. Understudies intrigued by DSP are urged to take however many of these "center" courses as could reasonably be expected. Also, there are a few other propelled flag handling classes that merit investigating: E.g.: Computer Vision, Speech Processing, Signal Recovery

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So, what is this class? Picture and Video Coding and Processing will cover: Multidimensional inspecting and sifting Models for the Human Visual System Color Modeling and Representation of Images Denoising Pattern acknowledgment Image and Video Compression Watermarking

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Course Resources Required Textbook: Y. Wang, J. Ostermann, Y-Q. Zhang: Digital Video Processing and Communications , Prentice-Hall, 2001. Reference Textbook: A.Bovik & J.Gibson: Handbook Of Image & Video Processing , Academic Press, 2000. The course book won't be adequate for this class (particularly at an early stage). I will dole out paper readings to supplement the content More regularly than not, these papers will originate from IEEE or ACM Most likely, I will make the papers accessible on the course site Other helpful asset: MATLAB's Image Processing Toolbox Goto www.mathworks.com

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The Dirty Work… This class is not a "cake-walk" graduate course: It ought not be expected that I will give out programmed A's. Truth be told, much the same as other "Center" courses, there could be C's in this class. Picture and Video Processing requires a considerable measure of work to comprehend: Programming in MATLAB is an absolute necessity Programming in C/C++ is exceedingly alluring (Matlab is quite recently too moderate for the monstrous enhancements you will require later) Seeing is having confidence in Image and Video Processing… Telling me that the MSE/PSNR is great is pleasant, yet regardless I need to see the picture! Seeing pictures and recordings play is a large portion of the good times!

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Introduction to Image and Video Processing Thanks to Min Wu (UMD) for giving a large number of the slides that take after.

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Images and Videos are Efficient and Effective Visual portrayals are frequently the most proficient approach to speak to data Images are utilized as a part of numerous situations: Politics, commercials, logical research, military, and so forth… Video signs can viably recount a transiently advancing story Arise in silver screen (films) Arise in observation Arise in restorative applications Signature of Joseph Brahms Sonar Image A doctored political advertisement http://marsrovers.jpl.nasa.gov/exhibition/squeeze/opportunity/20040125a.html JPL Mars' Panorama caught by the Opportunity

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Why Do We Process Images? Upgrade and reclamation Remove curios and scratches from an old photograph/motion picture Improve differentiate and adjust obscured pictures Transmission and capacity Images and Video can be all the more adequately transmitted and put away Information investigation and computerized acknowledgment Recognizing psychological militants Evidence Careful picture control can uncover data not present Detect picture altering Security and rights assurance Encryption and watermarking forestalling illicit substance control

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Compression Color picture of 600x800 pixels Without pressure 600*800 * 24 bits/pixel = 11.52K bits = 1.44M bytes After JPEG pressure (prevalently utilized on web) just 89K bytes pressure proportion ~ 16:1 Movie 720x480 for each casing, 30 outlines/sec, 24 bits/pixel Raw video ~ 243M bits/sec DVD ~ around 5M bits/sec Compression proportion ~ 48:1 "Library of Congress" by M.Wu (600x800)

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Denoising From X.Li http://www.ee.princeton.edu/~lixin/denoising.htm

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Deblurring From Mathworks

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Visual Mosaicing Stitch photographs together without string or scotch tape R.Radke – IEEE PRMI diary paper draft 5/01

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Visible Digital Watermarks from IBM Watson website page "Vatican Digital Library"

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Invisible Watermark first & 30th Mpeg4.5Mbps edge of unique, stamped, and their luminance contrast human visual model for impalpability: ensure smooth zones and sharp edges

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(a) unique lenna picture (b) adulterated lenna picture (c) disguised lenna picture 25% pieces in a checkerboard example are defiled debased squares are hidden by means of edge-coordinated addition Error Concealment Examples were produced utilizing the source codes gave by W.Zeng.

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y x I(x,y) y x What is An Image? Grayscale picture A grayscale picture is a capacity I(x,y) of the two spatial directions of the picture plane. I(x,y) is the force of the picture at the point (x,y) on the picture plane. I(x,y) takes non-negative qualities expect the picture is limited by a rectangle [0,a]  [0,b] I: [0, a]  [0, b]  [0, inf ) Color picture Can be spoken to by three capacities, R(x,y) for red, G(x,y) for green , and B(x,y) for blue.

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255 (white) 0 (dark) Sampling and Quantization Computer handles "discrete" information. Inspecting Sample the estimation of the picture at the hubs of a consistent network on the picture plane. A pixel (picture component) at (i, j) is the picture power an incentive at lattice point filed by the number facilitate (i, j). Quantization Is a procedure of changing a genuine esteemed tested picture to one taking just a limited number of particular qualities. Each inspected an incentive in a 256-level grayscale picture is spoken to by 8 bits.

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256x256 64x64 16x16 Examples of Sampling

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8 bits/pixel 4 bits/pixel 2 bits/pixel Examples of Quantizaion