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Image and Video Processing – An Introduction

Image and Video Processing – An Introduction. Fall ‘ 13 Instructor: Dr. Engr. Junaid Zafar Electrical Engineering Department Government College University, Lahore. Scope of EE-7105. First graduate course on image/video processing

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Image and Video Processing – An Introduction

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  1. Image and Video Processing –An Introduction Fall ‘13 Instructor: Dr. Engr. Junaid Zafar Electrical Engineering Department Government College University, Lahore

  2. Scope of EE-7105 • First graduate course on image/video processing • Not assume you have much exposure on image processing at undergraduate level • Require and build on background in random process and DSP • 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 recognition • multimedia communications and security

  3. Textbooks and References • R.C. Gonzalez and R.E. Woods: Digital Image Processing, Prentice Hall, 3rd Edition, 2008. • J. G. Proakis and D.G Manolakis: Digital Signal Processing, Principles, Algorithms & Applications, 4th Edition. • Related technical publications (will be announced in class). • Other related textbooks • Y. Wang, J. Ostermann, Y-Q. Zhang: Digital Video Processing and Communications, Prentice Hall, 2001. • A. K. Jain: Fundamentals of Digital Image Processing, Prentice Hall, 1989. • John W. Woods: Multidimensional Signal, Image, and Video Processing and Coding, Academic Press, 2006. • A. Bovik: Handbook Of Image & Video Processing, 2nd Edition, Academic Press, 2005.

  4. Image and Video Processing: An Introduction and Overview

  5. Why Do We Process Images? • Enhancement and restoration • Remove artifacts and scratches from an old photo/movie • Improve contrast and correct blurred images • Composition (for magazines and movies), Display, Printing … • Transmission and storage • images from oversea via Internet, or from a remote planet • Information analysis and automated recognition • Providing “human vision” to machines • Medical imaging for diagnosis and exploration • Security, forensics and rights protection • Encryption, hashing, digital watermarking, digital fingerprinting …

  6. Why Digital? • “Exactness” • Perfect reproduction without degradation • Perfect duplication of processing result • Convenient & powerful computer-aided processing • Can perform sophisticated processing through computer hardware or software • Even kindergartners can do some! • Easy storage and transmission • 1 CD can store hundreds of family photos! • Paperless transmission of high quality photos through network within seconds

  7. Examples of Digital Image & Video Processing • 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

  8. Compression • 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 bytes • compression ratio ~ 16:1 • Movie ~ Image Sequence • 720x 480 per frame, 30 frames/sec, 24 bits/pixel • Raw video ~ 243M bits/sec • DVD ~ about 5M bits/sec • Compression ratio ~ 48:1

  9. Denoising

  10. Deblurring

  11. Visual Mosaicing

  12. Face Detection • Image enhancement, feature extractions, and statistical modeling are often important steps in computer vision tasks

  13. Visible Digital Watermarks

  14. Invisible Watermark • Original, marked, and their amplified luminance difference • human visual model for imperceptibility: protect smooth areas and sharp edges

  15. (b) corrupted lenna image (c) concealed lenna image (a) original lenna image 25% blocks in a checkerboard pattern are corrupted corrupted blocks are concealed via edge-directed interpolation Error Concealment

  16. Different Ways to View an Image Intensity visualization over 2-D (x,y) plane In 3-D (x,y, z) plot with z=I(x,y);red color for high value and blue for low Equal value contour in (x,y) plane

  17. 255 (white) 0 (black) Sampling and Quantization • Computer handles “discrete” 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.

  18. 16x16 64x64 256x256 Examples of Sampling

  19. 8 bits / pixel 4 bits / pixel 2 bits / pixel Examples of Quantizaion

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