book

Digital Video Processing, Second Edition

Name: Digital Video Processing, Second Edition
Author: A. Murat Tekalp
ISBN: 9780133991116

by A. Murat Tekalp

June 2015

Intermediate to advanced

592 pages

17h 5m

English

Pearson

Read now

Unlock full access

About This eBook
Title Page
Copyright Page
Dedication Page
Contents
Preface
About the Author
Chapter 1. Multi-Dimensional Signals and Systems
1.1 Multi-Dimensional Signals1.1.1 Finite-Extent Signals and Periodic Signals1.1.2 Symmetric Signals1.1.3 Special Multi-Dimensional Signals1.2 Multi-Dimensional Transforms1.2.1 Fourier Transform of Continuous Signals1.2.2 Fourier Transform of Discrete Signals1.2.3 Discrete Fourier Transform (DFT)1.2.4 Discrete Cosine Transform (DCT)1.3 Multi-Dimensional Systems1.3.1 Impulse Response and 2D Convolution1.3.2 Frequency Response1.3.3 FIR Filters and Symmetry1.3.4 IIR Filters and Partial Difference Equations1.4 Multi-Dimensional Sampling Theory1.4.1 Sampling on a Lattice1.4.2 Spectrum of Signals Sampled on a Lattice1.4.3 Nyquist Criterion for Sampling on a Lattice1.4.4 Reconstruction from Samples on a Lattice1.5 Sampling Structure ConversionReferencesExercisesProblem Set 1MATLAB Exercises
Chapter 2. Digital Images and Video
2.1 Human Visual System and Color2.1.1 Color Vision and Models2.1.2 Contrast Sensitivity2.1.3 Spatio-Temporal Frequency Response2.1.4 Stereo/Depth Perception2.2 Analog Video2.2.1 Progressive vs. Interlaced Scanning2.2.2 Analog-Video Signal Formats2.2.3 Analog-to-Digital Conversion2.3 Digital Video2.3.1 Spatial Resolution and Frame Rate2.3.2 Color, Dynamic Range, and Bit-Depth2.3.3 Color Image Processing2.3.4 Digital-Video Standards2.4 3D Video2.4.1 3D-Display Technologies2.4.2 Stereoscopic Video2.4.3 Multi-View Video2.5 Digital-Video Applications2.5.1 Digital TV2.5.2 Digital Cinema2.5.3 Video Streaming over the Internet2.5.4 Computer Vision and Scene/Activity Understanding2.6 Image and Video Quality2.6.1 Visual Artifacts2.6.2 Subjective Quality Assessment2.6.3 Objective Quality AssessmentReferences
Chapter 3. Image Filtering
3.1 Image Smoothing3.1.1 Linear Shift-Invariant Low-Pass Filtering3.1.2 Bi-Lateral Filtering3.2 Image Re-Sampling and Multi-Resolution Representations3.2.1 Image Decimation3.2.2 Interpolation3.2.3 Multi-Resolution Pyramid Representations3.2.4 Wavelet Representations3.3 Image-Gradient Estimation, Edge and Feature Detection3.3.1 Estimation of the Image Gradient3.3.2 Estimation of the Laplacian3.3.3 Canny Edge Detection3.3.4 Harris Corner Detection3.4 Image Enhancement3.4.1 Pixel-Based Contrast Enhancement3.4.2 Spatial Filtering for Tone Mapping and Image Sharpening3.5 Image Denoising3.5.1 Image and Noise Models3.5.2 Linear Space-Invariant Filters in the DFT Domain3.5.3 Local Adaptive Filtering3.5.4 Nonlinear Filtering: Order-Statistics, Wavelet Shrinkage, and Bi-Lateral Filtering3.5.5 Non-Local Filtering: NL-Means and BM3D3.6 Image Restoration3.6.1 Blur Models3.6.2 Restoration of Images Degraded by Linear Space-Invariant Blurs3.6.3 Blind Restoration – Blur Identification3.6.4 Restoration of Images Degraded by Space-Varying Blurs3.6.5 Image In-PaintingReferencesExercisesProblem Set 3MATLAB ExercisesMATLAB Resources

Chapter 4. Motion Estimation
4.1 Image Formation4.1.1 Camera Models4.1.2 Photometric Effects of 3D Motion4.2 Motion Models4.2.1 Projected Motion vs. Apparent Motion4.2.2 Projected 3D Rigid-Motion Models4.2.3 2D Apparent-Motion Models4.3 2D Apparent-Motion Estimation4.3.1 Sparse Correspondence, Optical-Flow Estimation, and Image-Registration Problems4.3.2 Optical-Flow Equation and Normal Flow4.3.3 Displaced-Frame Difference4.3.4 Motion Estimation is Ill-Posed: Occlusion and Aperture Problems4.3.5 Hierarchical Motion Estimation4.3.6 Performance Measures for Motion Estimation4.4 Differential Methods4.4.1 Lukas–Kanade Method4.4.2 Horn–Schunk Motion Estimation4.5 Matching Methods4.5.1 Basic Block-Matching4.5.2 Variable-Size Block-Matching4.5.3 Hierarchical Block-Matching4.5.4 Generalized Block-Matching – Local Deformable Motion4.5.5 Homography Estimation from Feature Correspondences4.6 Nonlinear Optimization Methods4.6.1 Pel-Recursive Motion Estimation4.6.2 Bayesian Motion Estimation4.7 Transform-Domain Methods4.7.1 Phase-Correlation Method4.7.2 Space-Frequency Spectral Methods4.8 3D Motion and Structure Estimation4.8.1 Camera Calibration4.8.2 Affine Reconstruction4.8.3 Projective Reconstruction4.8.4 Euclidean Reconstruction4.8.5 Planar-Parallax and Relative Affine Structure Reconstruction4.8.6 Dense Structure from StereoReferencesExercisesProblem Set 4MATLAB ExercisesMATLAB Resources
Chapter 5. Video Segmentation and Tracking
5.1 Image Segmentation5.1.1 Thresholding5.1.2 Clustering5.1.3 Bayesian Methods5.1.4 Graph-Based Methods5.1.5 Active-Contour Models5.2 Change Detection5.2.1 Shot-Boundary Detection5.2.2 Background Subtraction5.3 Motion Segmentation5.3.1 Dominant-Motion Segmentation5.3.2 Multiple-Motion Segmentation5.3.3 Region-Based Motion Segmentation: Fusion of Color and Motion5.3.4 Simultaneous Motion Estimation and Segmentation5.4 Motion Tracking5.4.1 Graph-Based Spatio-Temporal Segmentation and Tracking5.4.2 Kanade–Lucas–Tomasi Tracking5.4.3 Mean-Shift Tracking5.4.4 Particle-Filter Tracking5.4.5 Active-Contour Tracking5.4.6 2D-Mesh Tracking5.5 Image and Video Matting5.6 Performance EvaluationReferencesMATLAB ExercisesInternet Resources
Chapter 6. Video Filtering
6.1 Theory of Spatio-Temporal Filtering6.1.1 Frequency Spectrum of Video6.1.2 Motion-Adaptive Filtering6.1.3 Motion-Compensated Filtering6.2 Video-Format Conversion6.2.1 Down-Conversion6.2.2 De-Interlacing6.2.3 Frame-Rate Conversion6.3 Multi-Frame Noise Filtering6.3.1 Motion-Adaptive Noise Filtering6.3.2 Motion-Compensated Noise Filtering6.4 Multi-Frame Restoration6.4.1 Multi-Frame Modeling6.4.2 Multi-Frame Wiener Restoration6.5 Multi-Frame Super-Resolution6.5.1 What Is Super-Resolution?6.5.2 Modeling Low-Resolution Sampling6.5.3 Super-Resolution in the Frequency Domain6.5.4 Multi-Frame Spatial-Domain MethodsReferencesExercisesProblem Set 6MATLAB Exercises
Chapter 7. Image Compression
7.1 Basics of Image Compression7.1.1 Information Theoretic Concepts7.1.2 Elements of Image-Compression Systems7.1.3 Quantization7.1.4 Symbol Coding7.1.5 Huffman Coding7.1.6 Arithmetic Coding7.2 Lossless Image Compression7.2.1 Bit-Plane Coding7.2.2 Run-Length Coding and ITU G3/G4 Standards7.2.3 Adaptive Arithmetic Coding and JBIG7.2.4 Early Lossless Predictive Coding7.2.5 JPEG-LS Standard7.2.6 Lempel–Ziv Coding7.3 Discrete-Cosine Transform Coding and JPEG7.3.1 Discrete-Cosine Transform7.3.2 ISO JPEG Standard7.3.3 Encoder Control and Compression Artifacts7.4 Wavelet-Transform Coding and JPEG20007.4.1 Wavelet Transform and Choice of Filters7.4.2 ISO JPEG2000 StandardReferencesExercisesInternet Resources
Chapter 8. Video Compression
8.1 Video-Compression Approaches8.1.1 Intra-Frame Compression, Motion JPEG 2000, and Digital Cinema8.1.2 3D-Transform Coding8.1.3 Motion-Compensated Transform Coding8.2 Early Video-Compression Standards8.2.1 ISO and ITU Standards8.2.2 MPEG-1 Standard8.2.3 MPEG-2 Standard8.3 MPEG-4 AVC/ITU-T H.264 Standard8.3.1 Input-Video Formats and Data Structure8.3.2 Intra-Prediction8.3.3 Motion Compensation8.3.4 Transform8.3.5 Other Tools and Improvements8.4 High-Efficiency Video-Coding (HEVC) Standard8.4.1 Video-Input Format and Data Structure8.4.2 Coding-Tree Units8.4.3 Tools for Parallel Encoding/Decoding8.4.4 Other Tools and Improvements8.5 Scalable-Video Compression8.5.1 Temporal Scalability8.5.2 Spatial Scalability8.5.3 Quality (SNR) Scalability8.5.4 Hybrid Scalability8.6 Stereo and Multi-View Video Compression8.6.1 Frame-Compatible Stereo-Video Compression8.6.2 Stereo and Multi-View Video-Coding Extensions of the H.264/AVC Standard8.6.3 Multi-View Video Plus Depth CompressionReferencesExercisesInternet Resources
Appendix A. Vector-Matrix Operations in Image and Video Processing
A.1 Two-Dimensional ConvolutionA.2 Two-Dimensional Discrete-Fourier TransformA.2.1 Diagonalization of Block-Circulant MatricesA.3 Three-Dimensional Rotation – Rotation MatrixA.3.1 Euler AnglesA.3.2 Rotation About an Arbitrary AxisA.3.3 Quaternion RepresentationReferencesExercises
Appendix B. Ill-Posed Problems in Image and Video Processing
B.1 Image RepresentationsB.1.1 Deterministic Framework – Function/Vector SpacesB.1.2 Bayesian Framework – Random FieldsB.2 Overview of Image ModelsB.3 Basics of Sparse-Image ModelingB.4 Well-Posed Formulations of Ill-Posed ProblemsB.4.1 Constrained-Optimization ProblemB.4.2 Bayesian-Estimation ProblemReferences
Appendix C. Markov and Gibbs Random Fields
C.1 Equivalence of Markov Random Fields and Gibbs Random FieldsC.1.1 Markov Random FieldsC.1.2 Gibbs Random FieldsC.1.3 Equivalence of MRF and GRFC.2 Gibbs Distribution as an a priori pdf ModelC.3 Computation of Local Conditional Probabilities from a Gibbs DistributionReferences
Appendix D. Optimization Methods
D.1 Gradient-Based OptimizationD.1.1 Steepest-Descent MethodD.1.2 Newton–Raphson MethodD.2 Simulated AnnealingD.2.1 Metropolis AlgorithmD.2.2 Gibbs SamplerD.3 Greedy MethodsD.3.1 Iterated Conditional ModesD.3.2 Mean-Field AnnealingD.3.3 Highest Confidence FirstReferences
Appendix E. Model Fitting
E.1 Least-Squares FittingE.2 Least-Squares Solution of Homogeneous Linear EquationsE.2.1 Alternate DerivationE.3 Total Least-Squares FittingE.4 Random-Sample Consensus (RANSAC)References
Index

Overview

This is the Rough Cut version of the printed book.

In the nearly twenty years since the first edition of Digital Video Processing, digital video has become ubiquitous, and the most successful techniques and algorithms for different video processing tasks have become more clear. Digital Video Processing, Second Edition, significantly improves the organization of the material and the presentation style in a truly tutorial manner and updates the technical content with the most recent knowledge of techniques, successful algorithms, and up-to-date information in order to once again position the book as necessary to your library.

This is a unique, comprehensive textbook, written by a single author in tutorial style. It covers fundamentals of digital image and video processing with equal emphasis on image filtering, motion estimation, video segmentation, video filtering, and image/video compression. After reading the book, the reader will

understand theoretical foundations of image and video processing methods

learn the most popular and successful techniques to solve common image and video processing problems

reinforce their understanding by solving problems and doing MATLAB projects at the end of each chapter

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Handbook of Image and Video Processing, 2nd Edition

Publisher Resources

ISBN: 9780133991116Purchase book

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills