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ENEE631 Digital Image Processing (Spring'06)

Digital Image and Video Processing Digital Image and Video Processing ––An IntroductionAn Introduction

Spring ’06 Instructor: K. J. Ray LiuECE Department, Univ. of Maryland, College Park

ENEE631 Digital Image Processing (Spring'06) Lec1 – Introduction [2]

Scope of ENEE631Scope of ENEE631

First graduate course on image/video processing

Prerequisites: ENEE620 and 624, or by permission– Not assume you have much exposure on image processing at

undergraduate level– Random processes and DSP are required background

Emphasis on fundamental concepts– Provide theoretical foundations on multi-dimensional signal

processing built upon pre-requisites– Coupled with assignments and projects for hands-on experience

and reinforcement of the concepts

– Follow-up courses image analysis, computer vision, pattern recognitionmultimedia communications and security


TextbooksTextbooks

Primary– A.K. Jain: Fundamentals of Digital Image Processing, Prentice-Hall,

1989. (blue cover)– R.C. Gonzalez and R.E. Woods: Digital Image Processing, Prentice Hall,

2001. (black cover)

– Y. Wang, J. Ostermann, Y-Q. Zhang: Digital Video Processing and Communications, Prentice-Hall, 2001.

References – A.Bovik & J.Gibson: Handbook Of Image & Video Processing,

Academic Press, 2000.

Other references– Will be announced in lectures


Introduction to Image and Video ProcessingIntroduction to Image and Video ProcessingU

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A picture is worth 1000 words.A picture is worth 1000 words.

Rich info. from visual dataExamples of images around usnatural photographic images; artistic and engineering drawingsscientific images (satellite, medical, etc.)

“Motion pictures” => videomovie, TV program; family video; surveillance and highway/ferry camera

A video is worth 1000 sentences?A video is worth 1000 sentences?

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http://marsrovers.jpl.nasa.gov/gallery/press/opportunity/20040125a.htmlJPL Mars’ Panorama captured by the Opportunity


Why Do We Process Images?Why Do We Process Images?

Enhancement and restoration– remove artifacts and scratches from an old photo/movie– improve contrast and correct blurred images

Transmission and storage– images from oversea via Internet, or from a remote planet

Information analysis and automated recognition– providing “human vision” to machines

Security and rights protection– encryption and watermarking

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Why Digital?Why Digital?

“Exactness”– Perfect reproduction without degradation– Perfect duplication of processing result

Convenient & powerful computer-aided processing– Can perform rather sophisticated processing through hardware or

software– Even kindergartners can do it!

Easy storage and transmission– 1 CD can store hundreds of family photos!– Paperless transmission of high quality photos through network within

seconds

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List of Image and Video Processing ExamplesList of Image and Video Processing Examples

Compression

Manipulation and Restoration– Restoration of blurred and damaged images– Noise removal and reduction– Morphing

Applications– Visual mosaicing and virtual views– Face detection– Visible and invisible watermarking– Error concealment and resilience in video transmission

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CompressionCompression

Color image of 600x800 pixels– Without compression

600*800 * 24 bits/pixel= 11.52K bits = 1.44M bytes

– After JPEG compression (popularly used on web)

only 89K bytescompression ratio ~ 16:1

Movie – 720x480 per frame, 30 frames/sec,

24 bits/pixel– Raw video ~ 243M bits/sec– DVD ~ about 5M bits/sec– Compression ratio ~ 48:1

“Library of Congress” by M.Wu (600x800)

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DenoisingDenoising

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

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DeblurringDeblurring

http://www.mathworks.com/access/helpdesk/help/toolbox/images/deblurr7.shtml

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MorphingMorphing

Princeton CS426 face morphing exampleshttp://www.cs.princeton.edu/courses/archive/fall98/cs426/assignments/morph/morph_results.html

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Visual Visual MosaicingMosaicing– Stitch photos together without thread or scotch tape

R.Radke – IEEE PRMI journal paper draft 5/01

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Face DetectionFace Detection

Face detection in ’98 @ CMU CS, http://www.cs.cmu.edu/afs/cs/Web/People/har/faces.html

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Visible Digital WatermarksVisible Digital Watermarks

from IBM Watson web page“Vatican Digital Library”

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Invisible Watermark Invisible Watermark

– 1st & 30th Mpeg4.5Mbps frame of original, marked, and their luminance difference– human visual model for imperceptibility: protect smooth areas and sharp edges

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Data Hiding for Annotating Binary Line DrawingsData Hiding for Annotating Binary Line Drawings

originaloriginal marked w/ marked w/ ““01/01/200001/01/2000””

pixelpixel--wise wise differencedifference

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Error ConcealmentError Concealment

25% blocks in a checkerboard pattern are corrupted

corrupted blocks are concealed via edge-directed interpolation

(a) original lenna image (c) concealed lenna image(b) corrupted lenna image

Examples were generated using the source codes provided by W.Zeng.


What is An Image?What is An Image?Grayscale image– A grayscale image is a function

I(x,y) of the two spatial coordinates of the image plane.

– I(x,y) is the intensity of the image at the point (x,y) on the image plane.

I(x,y) takes non-negative values

assume the image is bounded by a rectangle [0,a]×[0,b]

I: [0, a] × [0, b] → [0, inf )

Color image– Can be represented by three functions, R(x,y) for red, G(x,y) for green, and B(x,y) for blue.

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Sampling and QuantizationSampling and Quantization

Computer handles “digital” data.

Sampling– Sample the value of the image at the nodes of a

regular grid on the image plane.

– A pixel (picture element) at (i, j) is the image intensity value at grid point indexed by the integer coordinate (i, j).

Quantization– Is a process of transforming a real valued sampled

image to one taking only a finite number of distinct values.

– Each sampled value in a 256-level grayscale image is represented by 8 bits. 0 (black)

255 (white)

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Examples of SamplingExamples of Sampling

256x256

64x64

16x16


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Examples of Examples of QuantizaionQuantizaion

8 bits / pixel

4 bits / pixel

2 bits / pixel

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