Digital Video Processing, Second Edition

Digital Video Processing, Second Edition

by A. Murat Tekalp
Released June 2015
Publisher(s): Pearson
ISBN: 9780133991116

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

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

    • Table of contents

    1. About This eBook
    2. Title Page
    3. Copyright Page
    4. Dedication Page
    5. Contents
    6. Preface
    7. About the Author
    8. Chapter 1. Multi-Dimensional Signals and Systems
      1. 1.1 Multi-Dimensional Signals
        1. 1.1.1 Finite-Extent Signals and Periodic Signals
        2. 1.1.2 Symmetric Signals
        3. 1.1.3 Special Multi-Dimensional Signals
      2. 1.2 Multi-Dimensional Transforms
        1. 1.2.1 Fourier Transform of Continuous Signals
        2. 1.2.2 Fourier Transform of Discrete Signals
        3. 1.2.3 Discrete Fourier Transform (DFT)
        4. 1.2.4 Discrete Cosine Transform (DCT)
      3. 1.3 Multi-Dimensional Systems
        1. 1.3.1 Impulse Response and 2D Convolution
        2. 1.3.2 Frequency Response
        3. 1.3.3 FIR Filters and Symmetry
        4. 1.3.4 IIR Filters and Partial Difference Equations
      4. 1.4 Multi-Dimensional Sampling Theory
        1. 1.4.1 Sampling on a Lattice
        2. 1.4.2 Spectrum of Signals Sampled on a Lattice
        3. 1.4.3 Nyquist Criterion for Sampling on a Lattice
        4. 1.4.4 Reconstruction from Samples on a Lattice
      5. 1.5 Sampling Structure Conversion
      6. References
      7. Exercises
        1. Problem Set 1
        2. MATLAB Exercises
    9. Chapter 2. Digital Images and Video
      1. 2.1 Human Visual System and Color
        1. 2.1.1 Color Vision and Models
        2. 2.1.2 Contrast Sensitivity
        3. 2.1.3 Spatio-Temporal Frequency Response
        4. 2.1.4 Stereo/Depth Perception
      2. 2.2 Analog Video
        1. 2.2.1 Progressive vs. Interlaced Scanning
        2. 2.2.2 Analog-Video Signal Formats
        3. 2.2.3 Analog-to-Digital Conversion
      3. 2.3 Digital Video
        1. 2.3.1 Spatial Resolution and Frame Rate
        2. 2.3.2 Color, Dynamic Range, and Bit-Depth
        3. 2.3.3 Color Image Processing
        4. 2.3.4 Digital-Video Standards
      4. 2.4 3D Video
        1. 2.4.1 3D-Display Technologies
        2. 2.4.2 Stereoscopic Video
        3. 2.4.3 Multi-View Video
      5. 2.5 Digital-Video Applications
        1. 2.5.1 Digital TV
        2. 2.5.2 Digital Cinema
        3. 2.5.3 Video Streaming over the Internet
        4. 2.5.4 Computer Vision and Scene/Activity Understanding
      6. 2.6 Image and Video Quality
        1. 2.6.1 Visual Artifacts
        2. 2.6.2 Subjective Quality Assessment
        3. 2.6.3 Objective Quality Assessment
      7. References
    10. Chapter 3. Image Filtering
      1. 3.1 Image Smoothing
        1. 3.1.1 Linear Shift-Invariant Low-Pass Filtering
        2. 3.1.2 Bi-Lateral Filtering
      2. 3.2 Image Re-Sampling and Multi-Resolution Representations
        1. 3.2.1 Image Decimation
        2. 3.2.2 Interpolation
        3. 3.2.3 Multi-Resolution Pyramid Representations
        4. 3.2.4 Wavelet Representations
      3. 3.3 Image-Gradient Estimation, Edge and Feature Detection
        1. 3.3.1 Estimation of the Image Gradient
        2. 3.3.2 Estimation of the Laplacian
        3. 3.3.3 Canny Edge Detection
        4. 3.3.4 Harris Corner Detection
      4. 3.4 Image Enhancement
        1. 3.4.1 Pixel-Based Contrast Enhancement
        2. 3.4.2 Spatial Filtering for Tone Mapping and Image Sharpening
      5. 3.5 Image Denoising
        1. 3.5.1 Image and Noise Models
        2. 3.5.2 Linear Space-Invariant Filters in the DFT Domain
        3. 3.5.3 Local Adaptive Filtering
        4. 3.5.4 Nonlinear Filtering: Order-Statistics, Wavelet Shrinkage, and Bi-Lateral Filtering
        5. 3.5.5 Non-Local Filtering: NL-Means and BM3D
      6. 3.6 Image Restoration
        1. 3.6.1 Blur Models
        2. 3.6.2 Restoration of Images Degraded by Linear Space-Invariant Blurs
        3. 3.6.3 Blind Restoration – Blur Identification
        4. 3.6.4 Restoration of Images Degraded by Space-Varying Blurs
        5. 3.6.5 Image In-Painting
      7. References
      8. Exercises
        1. Problem Set 3
        2. MATLAB Exercises
      9. MATLAB Resources
    11. Chapter 4. Motion Estimation
      1. 4.1 Image Formation
        1. 4.1.1 Camera Models
        2. 4.1.2 Photometric Effects of 3D Motion
      2. 4.2 Motion Models
        1. 4.2.1 Projected Motion vs. Apparent Motion
        2. 4.2.2 Projected 3D Rigid-Motion Models
        3. 4.2.3 2D Apparent-Motion Models
      3. 4.3 2D Apparent-Motion Estimation
        1. 4.3.1 Sparse Correspondence, Optical-Flow Estimation, and Image-Registration Problems
        2. 4.3.2 Optical-Flow Equation and Normal Flow
        3. 4.3.3 Displaced-Frame Difference
        4. 4.3.4 Motion Estimation is Ill-Posed: Occlusion and Aperture Problems
        5. 4.3.5 Hierarchical Motion Estimation
        6. 4.3.6 Performance Measures for Motion Estimation
      4. 4.4 Differential Methods
        1. 4.4.1 Lukas–Kanade Method
        2. 4.4.2 Horn–Schunk Motion Estimation
      5. 4.5 Matching Methods
        1. 4.5.1 Basic Block-Matching
        2. 4.5.2 Variable-Size Block-Matching
        3. 4.5.3 Hierarchical Block-Matching
        4. 4.5.4 Generalized Block-Matching – Local Deformable Motion
        5. 4.5.5 Homography Estimation from Feature Correspondences
      6. 4.6 Nonlinear Optimization Methods
        1. 4.6.1 Pel-Recursive Motion Estimation
        2. 4.6.2 Bayesian Motion Estimation
      7. 4.7 Transform-Domain Methods
        1. 4.7.1 Phase-Correlation Method
        2. 4.7.2 Space-Frequency Spectral Methods
      8. 4.8 3D Motion and Structure Estimation
        1. 4.8.1 Camera Calibration
        2. 4.8.2 Affine Reconstruction
        3. 4.8.3 Projective Reconstruction
        4. 4.8.4 Euclidean Reconstruction
        5. 4.8.5 Planar-Parallax and Relative Affine Structure Reconstruction
        6. 4.8.6 Dense Structure from Stereo
      9. References
      10. Exercises
        1. Problem Set 4
        2. MATLAB Exercises
      11. MATLAB Resources
    12. Chapter 5. Video Segmentation and Tracking
      1. 5.1 Image Segmentation
        1. 5.1.1 Thresholding
        2. 5.1.2 Clustering
        3. 5.1.3 Bayesian Methods
        4. 5.1.4 Graph-Based Methods
        5. 5.1.5 Active-Contour Models
      2. 5.2 Change Detection
        1. 5.2.1 Shot-Boundary Detection
        2. 5.2.2 Background Subtraction
      3. 5.3 Motion Segmentation
        1. 5.3.1 Dominant-Motion Segmentation
        2. 5.3.2 Multiple-Motion Segmentation
        3. 5.3.3 Region-Based Motion Segmentation: Fusion of Color and Motion
        4. 5.3.4 Simultaneous Motion Estimation and Segmentation
      4. 5.4 Motion Tracking
        1. 5.4.1 Graph-Based Spatio-Temporal Segmentation and Tracking
        2. 5.4.2 Kanade–Lucas–Tomasi Tracking
        3. 5.4.3 Mean-Shift Tracking
        4. 5.4.4 Particle-Filter Tracking
        5. 5.4.5 Active-Contour Tracking
        6. 5.4.6 2D-Mesh Tracking
      5. 5.5 Image and Video Matting
      6. 5.6 Performance Evaluation
      7. References
      8. MATLAB Exercises
      9. Internet Resources
    13. Chapter 6. Video Filtering
      1. 6.1 Theory of Spatio-Temporal Filtering
        1. 6.1.1 Frequency Spectrum of Video
        2. 6.1.2 Motion-Adaptive Filtering
        3. 6.1.3 Motion-Compensated Filtering
      2. 6.2 Video-Format Conversion
        1. 6.2.1 Down-Conversion
        2. 6.2.2 De-Interlacing
        3. 6.2.3 Frame-Rate Conversion
      3. 6.3 Multi-Frame Noise Filtering
        1. 6.3.1 Motion-Adaptive Noise Filtering
        2. 6.3.2 Motion-Compensated Noise Filtering
      4. 6.4 Multi-Frame Restoration
        1. 6.4.1 Multi-Frame Modeling
        2. 6.4.2 Multi-Frame Wiener Restoration
      5. 6.5 Multi-Frame Super-Resolution
        1. 6.5.1 What Is Super-Resolution?
        2. 6.5.2 Modeling Low-Resolution Sampling
        3. 6.5.3 Super-Resolution in the Frequency Domain
        4. 6.5.4 Multi-Frame Spatial-Domain Methods
      6. References
      7. Exercises
        1. Problem Set 6
        2. MATLAB Exercises
    14. Chapter 7. Image Compression
      1. 7.1 Basics of Image Compression
        1. 7.1.1 Information Theoretic Concepts
        2. 7.1.2 Elements of Image-Compression Systems
        3. 7.1.3 Quantization
        4. 7.1.4 Symbol Coding
        5. 7.1.5 Huffman Coding
        6. 7.1.6 Arithmetic Coding
      2. 7.2 Lossless Image Compression
        1. 7.2.1 Bit-Plane Coding
        2. 7.2.2 Run-Length Coding and ITU G3/G4 Standards
        3. 7.2.3 Adaptive Arithmetic Coding and JBIG
        4. 7.2.4 Early Lossless Predictive Coding
        5. 7.2.5 JPEG-LS Standard
        6. 7.2.6 Lempel–Ziv Coding
      3. 7.3 Discrete-Cosine Transform Coding and JPEG
        1. 7.3.1 Discrete-Cosine Transform
        2. 7.3.2 ISO JPEG Standard
        3. 7.3.3 Encoder Control and Compression Artifacts
      4. 7.4 Wavelet-Transform Coding and JPEG2000
        1. 7.4.1 Wavelet Transform and Choice of Filters
        2. 7.4.2 ISO JPEG2000 Standard
      5. References
      6. Exercises
      7. Internet Resources
    15. Chapter 8. Video Compression
      1. 8.1 Video-Compression Approaches
        1. 8.1.1 Intra-Frame Compression, Motion JPEG 2000, and Digital Cinema
        2. 8.1.2 3D-Transform Coding
        3. 8.1.3 Motion-Compensated Transform Coding
      2. 8.2 Early Video-Compression Standards
        1. 8.2.1 ISO and ITU Standards
        2. 8.2.2 MPEG-1 Standard
        3. 8.2.3 MPEG-2 Standard
      3. 8.3 MPEG-4 AVC/ITU-T H.264 Standard
        1. 8.3.1 Input-Video Formats and Data Structure
        2. 8.3.2 Intra-Prediction
        3. 8.3.3 Motion Compensation
        4. 8.3.4 Transform
        5. 8.3.5 Other Tools and Improvements
      4. 8.4 High-Efficiency Video-Coding (HEVC) Standard
        1. 8.4.1 Video-Input Format and Data Structure
        2. 8.4.2 Coding-Tree Units
        3. 8.4.3 Tools for Parallel Encoding/Decoding
        4. 8.4.4 Other Tools and Improvements
      5. 8.5 Scalable-Video Compression
        1. 8.5.1 Temporal Scalability
        2. 8.5.2 Spatial Scalability
        3. 8.5.3 Quality (SNR) Scalability
        4. 8.5.4 Hybrid Scalability
      6. 8.6 Stereo and Multi-View Video Compression
        1. 8.6.1 Frame-Compatible Stereo-Video Compression
        2. 8.6.2 Stereo and Multi-View Video-Coding Extensions of the H.264/AVC Standard
        3. 8.6.3 Multi-View Video Plus Depth Compression
      7. References
      8. Exercises
      9. Internet Resources
    16. Appendix A. Vector-Matrix Operations in Image and Video Processing
      1. A.1 Two-Dimensional Convolution
      2. A.2 Two-Dimensional Discrete-Fourier Transform
        1. A.2.1 Diagonalization of Block-Circulant Matrices
      3. A.3 Three-Dimensional Rotation – Rotation Matrix
        1. A.3.1 Euler Angles
        2. A.3.2 Rotation About an Arbitrary Axis
        3. A.3.3 Quaternion Representation
      4. References
      5. Exercises
    17. Appendix B. Ill-Posed Problems in Image and Video Processing
      1. B.1 Image Representations
        1. B.1.1 Deterministic Framework – Function/Vector Spaces
        2. B.1.2 Bayesian Framework – Random Fields
      2. B.2 Overview of Image Models
      3. B.3 Basics of Sparse-Image Modeling
      4. B.4 Well-Posed Formulations of Ill-Posed Problems
        1. B.4.1 Constrained-Optimization Problem
        2. B.4.2 Bayesian-Estimation Problem
      5. References
    18. Appendix C. Markov and Gibbs Random Fields
      1. C.1 Equivalence of Markov Random Fields and Gibbs Random Fields
        1. C.1.1 Markov Random Fields
        2. C.1.2 Gibbs Random Fields
        3. C.1.3 Equivalence of MRF and GRF
      2. C.2 Gibbs Distribution as an a priori pdf Model
      3. C.3 Computation of Local Conditional Probabilities from a Gibbs Distribution
      4. References
    19. Appendix D. Optimization Methods
      1. D.1 Gradient-Based Optimization
        1. D.1.1 Steepest-Descent Method
        2. D.1.2 Newton–Raphson Method
      2. D.2 Simulated Annealing
        1. D.2.1 Metropolis Algorithm
        2. D.2.2 Gibbs Sampler
      3. D.3 Greedy Methods
        1. D.3.1 Iterated Conditional Modes
        2. D.3.2 Mean-Field Annealing
        3. D.3.3 Highest Confidence First
      4. References
    20. Appendix E. Model Fitting
      1. E.1 Least-Squares Fitting
      2. E.2 Least-Squares Solution of Homogeneous Linear Equations
        1. E.2.1 Alternate Derivation
      3. E.3 Total Least-Squares Fitting
      4. E.4 Random-Sample Consensus (RANSAC)
      5. References
    21. Index

    Product information

    • Title: Digital Video Processing, Second Edition
    • Author(s): A. Murat Tekalp
    • Release date: June 2015
    • Publisher(s): Pearson
    • ISBN: 9780133991116