Image and Video Processing – An Introduction 9/18/2018 Image and Video Processing – An Introduction Fall ‘13 Instructor: Dr. Engr. Junaid Zafar Electrical Engineering Department Government College University, Lahore

9/18/2018 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

Textbooks and References 9/18/2018 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.

Image and Video Processing: An Introduction and Overview 9/18/2018 Image and Video Processing: An Introduction and Overview

Why Do We Process Images? 9/18/2018 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 …

Why Digital? 9/18/2018 “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

Examples of Digital Image & Video Processing 9/18/2018 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

Compression 9/18/2018 Movie ~ Image Sequence 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

9/18/2018 Denoising

9/18/2018 Deblurring

9/18/2018 Visual Mosaicing

9/18/2018 Face Detection Image enhancement, feature extractions, and statistical modeling are often important steps in computer vision tasks

Visible Digital Watermarks 9/18/2018 Visible Digital Watermarks

Invisible Watermark 9/18/2018 Original, marked, and their amplified luminance difference human visual model for imperceptibility: protect smooth areas and sharp edges

9/18/2018 Error 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

Different Ways to View an Image 9/18/2018 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

Sampling and Quantization 9/18/2018 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. 0 (black) 255 (white)

9/18/2018 Examples of Sampling 256x256 64x64 16x16

Examples of Quantizaion 9/18/2018 Examples of Quantizaion 8 bits / pixel 4 bits / pixel 2 bits / pixel