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E-grāmata: Image and Video Compression: Fundamentals, Techniques, and Applications

, , (Vishwakarma Institute of Information Technology, Pune, Maharashtra, India), (College of Engineering, Pune, Maharashtra, India),
  • Formāts: 236 pages
  • Izdošanas datums: 17-Nov-2014
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781482228236
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Image and Video Compression: Fundamentals, Techniques, and ApplicationsPalielināt
  • Formāts: 236 pages
  • Izdošanas datums: 17-Nov-2014
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781482228236
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"Preface This book is intended primarily for courses in image compression techniques for undergraduate through postgraduate students, research scholars, and engineers working in the field. It presents the basic concepts and technologies in a student-friendly manner. The major techniques in image compression are explained with informative illustrations, and the concepts are evolved from the basics. Practical implementation is demonstrated with MATLAB

Image and video signals require large transmission bandwidth and storage, leading to high costs. The data must be compressed without a loss or with a small loss of quality. Thus, efficient image and video compression algorithms play a significant role in the storage and transmission of data.

Image and Video Compression: Fundamentals, Techniques, and Applications explains the major techniques for image and video compression and demonstrates their practical implementation using MATLAB® programs. Designed for students, researchers, and practicing engineers, the book presents both basic principles and real practical applications.

In an accessible way, the book covers basic schemes for image and video compression, including lossless techniques and wavelet- and vector quantization-based image compression and digital video compression. The MATLAB programs enable readers to gain hands-on experience with the techniques. The authors provide quality metrics used to evaluate the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most important part of the signal while it is being sensed.

Recenzijas

"... a real tour of the field of multimedia compression, for both 2D and 3D images. ... While the authors are aiming to target 'students' with their text, it is important to note that this term is meant generically to include anyone interested in the field, from an undergraduate to a graduate (or even a postgraduate), from someone in academia to a researcher in the field. This requires a delicate balance between the theoretical and the practical, from the introductory level to the advanced. Reading the text, the reader will see that the authors have been able to walk this delicate tightrope. They accomplish this by including summaries, questions for thought, meaningful illustrations, chapter bibliographies for further reading, and perhaps most importantly, the inclusion of actual MATLAB code for practical implementation. ... The authors should be congratulated for this comprehensive yet accessible text." -Minette Carl and Robert Goldberg, Queens College, Flushing, New York, USA, from Computing Reviews, August 7, 2015

Preface xi
Authors xv
1 Introduction to Image Compression 1(4)
1.1 Need for Coding
1(1)
1.2 Measurement of Quality
1(1)
1.3 Lossless Compression
2(1)
1.4 Lossy Compression
2(2)
Bibliography
4(1)
2 Lossless Image Compression 5(18)
2.1 Introduction
5(1)
2.2 Source Encoders and Decoders
6(1)
2.3 Coding Redundancy
7(1)
2.4 Interpixel Redundancy
8(1)
2.5 Psychovisual Redundancy
8(1)
2.6 Image Compression Models
9(2)
2.7 Channel Encoder and Decoder Realization
11(1)
2.8 Information Theory
11(1)
2.8.1 Information
11(1)
2.9 Classification
12(9)
2.9.1 Compression Ratio
12(1)
2.9.2 Contents of a Picture
12(13)
2.9.2.1 Shannon-Fano Coding
13(1)
2.9.2.2 Huffman Coding
14(3)
2.9.2.3 Lempel-Ziv 77
17(2)
2.9.2.4 Arithmetic Coding
19(1)
2.9.2.5 Run-Length Coding
19(2)
Bibliography
21(2)
3 Image Transforms 23(38)
3.1 Introduction
23(2)
3.2 Fundamentals of Image Transforms
25(4)
3.2.1 Orthogonal Functions
25(1)
3.2.2 Unitary Matrix
26(1)
3.2.3 Unitary Transform
26(1)
3.2.4 One-Dimensional Signals
27(1)
3.2.5 Two-Dimensional Signals
27(2)
3.3 Two-Dimensional Image Transforms with a Fixed Basis
29(1)
3.4 Two-Dimensional Fourier Transforms
30(7)
3.4.1 Separability
31(2)
3.4.2 Translation
33(1)
3.4.3 Periodicity and Conjugate Properties
34(1)
3.4.4 Rotation
35(1)
3.4.5 Distributive and Scaling
36(1)
3.4.6 Average
36(1)
3.4.7 Convolution and Correlation
36(1)
3.4.8 Correlation
37(1)
3.5 Two-Dimensional Discrete Cosine Transforms
37(12)
3.5.1 Comparison of Discrete Cosine Transforms and Discrete Fourier Transforms
39(1)
3.5.2 Application: DC—AC Coefficients Based on Multiple Watermarking Technique
40(13)
3.5.2.1 Deciding the Step Size
41(2)
3.5.2.2 Watermark Embedding
43(1)
3.5.2.3 Watermark Decoding
44(1)
3.5.2.4 Experimentation and Results
44(2)
3.5.2.5 Conclusion and Discussion
46(3)
3.6 The Walsh-Hadamard Transform
49(2)
3.7 Optimal Transforms
51(2)
3.8 The Karhunen-Loeve Transform: Two-Dimensional Image Transform with a Data-Dependent Basis
53(3)
3.8.1 Properties of the Karhunen-Loeve Transform
53(12)
3.8.1.1 De-Correlation
53(1)
3.8.1.2 Minimizing the Mean Square Error with a Limited Basis
54(1)
3.8.1.3 Minimizing the Transmission Rate at a Given Noise
55(1)
3.9 Summary
56(1)
Bibliography
57(4)
4 Wavelet-Based Image Compression 61(20)
4.1 Introduction
61(1)
4.2 The Short-Time Fourier Transform
61(1)
4.3 Wavelets
62(1)
4.4 The Continuous Wavelet Transform
63(2)
4.5 Inverse Continuous Wavelet Transform
65(1)
4.6 The Discrete Wavelet Transform
65(6)
4.6.1 Wavelet Transform—Multiresolution Analysis
66(2)
4.6.1.1 Scaling Function
66(1)
4.6.1.2 Wavelet Function
67(1)
4.6.2 Properties of the Digital Filter
68(1)
4.6.3 Two-Dimensional Wavelet
69(2)
4.7 Wavelet Families
71(1)
4.8 Choice of Wavelet Function
71(2)
4.9 Discrete Wavelet Transform—Based Image Compression
73(2)
4.10 JPEG 2000 Image Compression Standard
75(3)
Questions
78(1)
Bibliography
79(2)
5 Image Compression Using Vector Quantization 81(30)
5.1 Introduction
81(1)
5.2 Theory of Vector Quantization
82(3)
5.2.1 Advantages of Vector Quantization
84(1)
5.2.2 Disadvantages of Vector Quantization
84(1)
5.3 Design of Vector Quantizers
85(3)
5.3.1 The Linde-Buzo-Gray Algorithm
85(2)
5.3.2 Other Methods of Designing VQ
87(1)
5.4 Tree-Structured Vector Quantizer
88(1)
5.5 Mean-Removed Vector Quantizer
89(1)
5.6 Gain-Shape Vector Quantization
90(1)
5.7 Classified Vector Quantizer
91(1)
5.8 Multistage Vector Quantizer
92(1)
5.9 Adaptive Vector Quantizer
93(1)
5.10 Hierarchical Vector Quantizer
93(1)
5.11 Predictive Vector Quantizer
94(1)
5.12 Transform Vector Quantizer
95(2)
5.13 Binary Vector Quantizer
97(1)
5.14 Variable-Rate Vector Quantization
98(1)
5.15 Artificial Neural Network Approaches to Vector Quantizer Design
98(4)
5.15.1 Introduction to Artificial Neural Networks
99(1)
5.15.2 Competitive Learning Algorithm
100(1)
5.15.3 Kohonen's Self-Organizing Feature Maps
100(2)
5.16 Concluding Remarks
102(1)
5.17 MATLAB Programs
102(6)
References
108(1)
Bibliography
109(2)
6 Digital Video Compression 111(26)
6.1 Introduction
111(1)
6.2 Digital Video Data
112(1)
6.3 Video Compression Techniques
113(1)
6.4 Perceptual Redundancies
113(1)
6.4.1 Temporal Perception
113(1)
6.4.2 Spatial Perception
114(1)
6.5 Exploiting Spatial Redundancies (Intraframe Coding Technique)
114(1)
6.5.1 Predictive Coding
114(1)
6.5.2 Transform Coding
115(1)
6.6 Exploiting Temporal Redundancies (Interframe Coding Technique)
115(3)
6.6.1 Interframe Predictive Coding
116(1)
6.6.2 Motion-Compensated Prediction
117(1)
6.7 Exploiting Statistical Redundancies
118(1)
6.8 Hybrid Video Coding
118(1)
6.9 Block Matching Motion Estimation
119(4)
6.9.1 Block Distortion Measure
121(1)
6.9.2 Block Size
122(1)
6.9.3 Search Range
123(1)
6.10 Motion Search Algorithms
123(1)
6.11 Exhaustive Search
124(1)
6.12 Two-Dimensional Logarithmic Search Algorithm
124(1)
6.13 Three-Step Search Algorithm
125(1)
6.14 Cross-Search Algorithm
125(1)
6.15 One-at-a-Time Search Algorithm
126(1)
6.16 Performance Comparison of Motion Search Algorithms
127(2)
6.17 Video Coding Standards
129(5)
6.17.1 H.261 and H.263
130(1)
6.17.2 MPEG-1 Video Coding Standard
131(1)
6.17.3 MPEG-2 Video Coding Standard
131(1)
6.17.4 MPEG-4
131(1)
6.17.5 MPEG-7
132(1)
6.17.6 H.264/MPEG-4 Part 10/AVC
132(1)
6.17.7 Scalable Video Coding
132(1)
6.17.8 Next-Generation Video Compression—HEVC
133(1)
References
134(3)
7 Image Quality Assessment 137(20)
7.1 Introduction
137(1)
7.2 Subjective Image Quality Analysis
138(1)
7.3 Objective Image Quality Assessment
139(1)
7.4 Full-Reference Image Quality Assessment
140(7)
7.4.1 Peak Signal-to-Noise Ratio, Mean Square Error, and Maximum Difference
140(1)
7.4.2 Why Mean Square Error Is Not Always Correct
141(1)
7.4.3 Spectral Activity Measure and Spatial Frequency Measure
142(1)
7.4.4 Normalized Cross-Correlation Measure
143(1)
7.4.5 Structural Content and Image Fidelity
143(1)
7.4.6 Mean Structural Similarity Index
144(1)
7.4.7 Singular Value Decomposition Measure
145(1)
7.4.8 Edge-Based Pratt Measure
146(1)
7.4.9 Entropy
146(1)
7.4.10 Mutual Information
147(1)
7.5 No Reference Image Quality Assessment
147(2)
7.6 Reduced-Reference Image Quality Assessment
149(1)
7.7 Concluding Remarks
150(1)
7.8 MATLAB Programs
151(2)
7.9 Case Studies
153(1)
References
154(1)
Bibliography
154(3)
8 Compressive Sensing 157(52)
8.1 Introduction
157(1)
8.2 Motivation for Compressive Sensing
157(4)
8.3 Basics of Simultaneous Compression and Sensing
161(3)
8.4 Basic Compressive Sensing Framework
164(3)
8.5 Heart of Compressed Sensing: Measurement Matrix
167(1)
8.6 Important Properties of the Measurement Matrix
168(3)
8.6.1 Spark
168(1)
8.6.2 Coherence
169(1)
8.6.3 Null Space Property
170(1)
8.7 Uniqueness Guarantee in the Presence of a Noise
171(2)
8.7.1 Restricted Isometric Property
171(2)
8.8 Building a Sensing Matrix
173(2)
8.9 Compressive Sensing Sparse Recovery
175(19)
8.9.1 Minimization Framework
176(7)
8.9.2 Greedy Sparse Recovery Algorithms
183(11)
8.10 Recovery Guarantees for Greedy Algorithms
194(2)
8.11 Semifragile Watermarking Technique Based on Compressed Sensing
196(8)
8.11.1 Watermark Embedding
199(1)
8.11.2 Watermark Detection
200(1)
8.11.3 Results and Observations
200(4)
8.12 Summary
204(1)
Bibliography
205(3)
Compressive Sensing Solvers
208(1)
Compressive Sensing Course Pages
208(1)
Index 209
Joshi, Madhuri A.; Raval, Mehul S.; Dandawate, Yogesh H.; Joshi, Kalyani R.; Metkar, Shilpa P.
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