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Image and Video Processing in the Compressed Domain [Kõva köide]

  • Formaat: Hardback, 302 pages, kõrgus x laius: 234x156 mm, kaal: 720 g, 40 Tables, black and white; 16 Illustrations, color; 86 Illustrations, black and white
  • Ilmumisaeg: 22-Mar-2011
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1439829357
  • ISBN-13: 9781439829356
Teised raamatud teemal:
Image and Video Processing in the Compressed DomainSuurem pilt
  • Formaat: Hardback, 302 pages, kõrgus x laius: 234x156 mm, kaal: 720 g, 40 Tables, black and white; 16 Illustrations, color; 86 Illustrations, black and white
  • Ilmumisaeg: 22-Mar-2011
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1439829357
  • ISBN-13: 9781439829356
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781439829356.html
Märksõnad:
As more images and videos are becoming available in compressed formats, researchers have begun designing algorithms for different image operations directly in their domains of representation, leading to faster computation and lower buffer requirements. Image and Video Processing in the Compressed Domain presents the fundamentals, properties, and applications of a variety of image transforms used in image and video compression. It illustrates the development of algorithms for processing images and videos in the compressed domain.

Developing concepts from first principles, the book introduces popular image and video compression algorithms, in particular JPEG, JPEG2000, MPEG-2, MPEG-4, and H.264 standards. It also explores compressed domain analysis and performance metrics for comparing algorithms. The author then elucidates the definitions and properties of the discrete Fourier transform (DFT), discrete cosine transform (DCT), integer cosine transform (ICT), and discrete wavelet transform (DWT). In the subsequent chapters, the author discusses core operations, such as image filtering, color enhancement, image resizing, and transcoding of images and videos, that are used in various image and video analysis approaches. He also focuses on other facets of compressed domain analysis, including video editing operations, video indexing, and image and video steganography and watermarking.

With MATLAB® codes on an accompanying CD-ROM, this book takes you through the steps involved in processing and analyzing compressed videos and images. It covers the algorithms, standards, and techniques used for coding images and videos in compressed formats.
1 Image and Video Compression: An Overview
1(48)
1.1 Compression: Generic Approaches
2(4)
1.1.1 Alternative Representation
3(2)
1.1.2 Quantization
5(1)
1.1.3 Entropy Coding
5(1)
1.1.4 Rate-Distortion Control
6(1)
1.2 Motivation for Processing in the Compressed Domain
6(2)
1.3 Overview of Different Image and Video Compression Techniques and Standards
8(1)
1.4 Image Compression Techniques
9(11)
1.4.1 Baseline Sequential JPEG Lossy Encoding Scheme
10(1)
1.4.1.1 Level Shifting
10(1)
1.4.1.2 Transformation
10(1)
1.4.1.3 Quantization
11(1)
1.4.1.4 Encoding DC Coefficients
12(1)
1.4.1.5 Encoding AC Coefficients
13(1)
1.4.1.6 Entropy Encoding
13(1)
1.4.1.7 Encoding Colors
14(1)
1.4.2 JPEG2000
15(1)
1.4.2.1 Discrete Wavelet Transform (DWT)
16(2)
1.4.2.2 Quantization
18(1)
1.4.2.3 Bit-Stream Layering, Packetization, and Entropy Coding
19(1)
1.4.2.4 Color Encoding
19(1)
1.5 Video Compression Techniques
20(21)
1.5.1 MPEG-2
22(1)
1.5.1.1 Encoding Structure
22(1)
1.5.1.2 Frame Types
23(2)
1.5.1.3 Method of Encoding Pictures
25(2)
1.5.1.4 Motion Estimation
27(1)
1.5.1.5 Handling Interlaced Video
28(1)
1.5.2 MPEG-4
29(1)
1.5.2.1 Video Object Layer
29(2)
1.5.2.2 Background Encoding
31(1)
1.5.2.3 Wavelet Encoding of Still Images
31(1)
1.5.3 H.264/AVC
31(1)
1.5.3.1 Slices and Slice Groups
32(1)
1.5.3.2 Additional Picture Types
32(1)
1.5.3.3 Adaptive Frame/Field-Coding Operation
32(3)
1.5.3.4 Intra-frame Prediction
35(2)
1.5.3.5 Inter-frame Prediction in P Slices
37(1)
1.5.3.6 Inter-frame Prediction in B Slices
38(1)
1.5.3.7 Integer Transform and Scaling
38(1)
1.5.3.8 Quantization
39(1)
1.5.3.9 Second Transformation of DC Coefficients
40(1)
1.5.3.10 Entropy Coding
40(1)
1.5.3.11 In-Loop Deblocking Filter
40(1)
1.5.3.12 Network Abstraction Layer
40(1)
1.6 Examples of a Few Operations in the Compressed Domain
41(2)
1.7 Issues and Performance Measures
43(5)
1.7.1 Complexity of Algorithms
43(1)
1.7.2 Quality of Processed Images or Videos
44(1)
1.7.2.1 Similarity with respect to a Benchmark or Reference Image
45(1)
1.7.2.2 Visibility of Artifacts
46(1)
1.7.2.3 Measure of Colorfulness
47(1)
1.7.3 Level of Compression of the Input and Output Data
48(1)
1.8 Summary
48(1)
2 Image Transforms
49(56)
2.1 Orthogonal Expansion of a Function
50(10)
2.1.1 Trivial Expansion with Dirac Delta Functions
53(1)
2.1.2 Fourier Series Expansion
53(1)
2.1.3 Fourier Transform
53(1)
2.1.3.1 Properties of Fourier Transform
54(3)
2.1.4 Shannon's Orthonormal Bases for Band-limited Functions
57(1)
2.1.5 Wavelet Bases
57(1)
2.1.5.1 Multiresolution Approximations
58(2)
2.1.5.2 Wavelet Bases for Multiresolution Approximations
60(1)
2.2 Transforms of Discrete Functions
60(37)
2.2.1 Discrete Fourier Transform (DFT)
61(1)
2.2.1.1 The Transform Matrix
62(1)
2.2.1.2 Discrete Fourier Transform as Fourier Series of a Periodic Function
62(1)
2.2.1.3 Circular Convolution
63(1)
2.2.1.4 Energy Preservation
64(1)
2.2.1.5 Other Properties
64(1)
2.2.2 Generalized Discrete Fourier Transform (GDFT)
65(1)
2.2.2.1 Transform Matrices
66(1)
2.2.2.2 Convolution-Multiplication Properties
66(1)
2.2.3 Discrete Trigonometric Transforms
67(1)
2.2.3.1 Symmetric Extensions of Finite Sequences
68(1)
2.2.3.2 Symmetric Periodic Extension
68(6)
2.2.3.3 Different Types of Discrete Trigonometric Transforms
74(3)
2.2.3.4 Convolution Multiplication Properties
77(2)
2.2.4 Type-II Even DCT
79(1)
2.2.4.1 Matrix Representation
79(1)
2.2.4.2 Downsampling and Upsampling Properties of the DCTs
79(1)
2.2.4.3 Subband Relationship of the type-II DCT
80(1)
2.2.4.4 Approximate DCT Computation
81(1)
2.2.4.5 Composition and Decomposition of the DCT Blocks
81(1)
2.2.4.6 Properties of Block Composition Matrices
82(4)
2.2.4.7 Matrix Factorization
86(1)
2.2.4.8 8-Point Type-II DCT Matrix (C8)
86(1)
2.2.4.9 Integer Cosine Transforms
87(2)
2.2.5 Hadamard Transform
89(1)
2.2.6 Discrete Wavelet Transform (DWT)
89(1)
2.2.6.1 Orthonormal Basis with a Single Mother Wavelet
89(1)
2.2.6.2 Orthonormal Basis with Two Mother Wavelets
90(1)
2.2.6.3 Haar Wavelets
90(1)
2.2.6.4 Other Wavelets
91(1)
2.2.6.5 DWT through Filter Banks
92(3)
2.2.6.6 Lifting-based DWT
95(2)
2.3 Transforms in 2-D Space
97(7)
2.3.1 2-D Discrete Cosine Transform
99(1)
2.3.1.1 Matrix Representation
99(1)
2.3.1.2 Subband Approximation of the Type-II DCT
99(1)
2.3.1.3 Composition and Decomposition of the DCT Blocks in 2-D
100(1)
2.3.1.4 Symmetric Convolution and Convolution-Multiplication Properties for 2-D DCT
100(1)
2.3.1.5 Fast DCT Algorithms
100(2)
2.3.2 2-D Discrete Wavelet Transform
102(1)
2.3.2.1 Computational Complexity
103(1)
2.4 Summary
104(1)
3 Image Filtering
105(30)
3.1 Linear Shift Invariant (LSI) Systems
106(1)
3.2 Discrete LSI Systems
107(1)
3.3 Filtering a Finite Length Sequence
108(9)
3.3.1 Extension by Zero Padding
108(1)
3.3.1.1 Linear Convolution Matrix
109(1)
3.3.2 Periodic Extension
110(1)
3.3.2.1 Circular Convolution Matrix
110(1)
3.3.2.2 Linear Convolution Performed through Circular Convolution
111(1)
3.3.3 Antiperiodic Extension
111(1)
3.3.3.1 Skew Circular Convolution Matrix
112(1)
3.3.3.2 Circular Convolution as a Series of Skew Circular Convolution
112(1)
3.3.4 Symmetric Extension
112(1)
3.3.4.1 Symmetric Convolution Matrices
113(2)
3.3.4.2 Linear Convolution through Symmetric Convolution
115(2)
3.4 Block Filtering
117(9)
3.4.1 Overlapping and Save Methods in the Transform Domain
118(1)
3.4.2 Overlapping and Add Methods in the Transform Domain
119(1)
3.4.2.1 Filtering with Symmetric FIR
120(3)
3.4.2.2 Filtering with Antisymmetric FIR
123(1)
3.4.2.3 Filtering with an Arbitrary FIR
124(1)
3.4.2.4 Efficient Computation
124(2)
3.5 Filtering 2-D Images
126(6)
3.5.1 Separable Filters
126(1)
3.5.1.1 Sparse Computation
127(1)
3.5.1.2 Computation through Spatial Domain
128(1)
3.5.1.3 Quality of Filtered Images with Sparse Computation
129(1)
3.5.2 Nonseparable Filters
130(2)
3.6 Application of Filtering
132(2)
3.6.1 Removal of Blocking Artifacts
132(1)
3.6.2 Image Sharpening
132(2)
3.7 Summary
134(1)
4 Color Processing
135(34)
4.1 Color Representation
136(1)
4.2 Color Space
137(3)
4.2.1 RGB Color Space
137(1)
4.2.2 CIE XYZ Color Space
137(1)
4.2.3 CIE Chromaticity Coordinates
138(1)
4.2.4 YCbCr Color Space
139(1)
4.3 Processing Colors in the Compressed Domain
140(1)
4.4 Color Saturation and Desaturation
140(6)
4.4.1 Normalized YCbCr Color Space
142(1)
4.4.2 Maximum Saturation
142(2)
4.4.3 Desaturation of Colors
144(1)
4.4.4 Computation in the Block DCT Space
144(1)
4.4.5 Computational Cost
145(1)
4.4.5.1 MaxSat
145(1)
4.4.5.2 SatDesat
145(1)
4.4.5.3 DCT-domain Techniques
146(1)
4.5 Color Constancy
146(7)
4.5.1 Estimating Spectral Components of a Single Illuminant
147(1)
4.5.1.1 Computation in the Block DCT Space
148(2)
4.5.1.2 Cost of Computation and Storage
150(1)
4.5.2 Color Correction
151(1)
4.5.2.1 Color Correction in the YCbCr Color Space
152(1)
4.5.2.2 Color Correction by Chromatic Shift
153(1)
4.6 Color Enhancement
153(6)
4.6.1 Alpha Rooting
154(1)
4.6.2 Multicontrast Enhancement
154(1)
4.6.3 Multicontrast Enhancement with Dynamic Range Compression
155(1)
4.6.4 Color Enhancement by Scaling DCT Coefficients
155(1)
4.6.4.1 Preservation of Contrast
155(1)
4.6.4.2 Preservation of Color
156(1)
4.6.4.3 The Algorithm
157(1)
4.6.5 Examples of Color Enhancement
158(1)
4.6.5.1 Iterative Enhancement
158(1)
4.7 Summary
159(10)
5 Image Resizing
169(26)
5.1 Image Halving and Image Doubling in the Compressed Domain
170(14)
5.1.1 Using Linear, Distributive and Unitary Transform Properties
170(2)
5.1.2 Using Convolution-Multiplication Properties
172(1)
5.1.2.1 Two-fold Downsampling of 8-point DCT Blocks in 1-D
173(2)
5.1.2.2 Twofold Upsampling of 8-point DCT Blocks in 1-D
175(1)
5.1.2.3 Example in 2-D
175(1)
5.1.3 Using Subband DCT Approximation with Block Composition and Decomposition
176(1)
5.1.3.1 Image Halving
177(2)
5.1.3.2 Image Doubling
179(3)
5.1.4 Performance Analysis
182(2)
5.2 Resizing with Integral Factors
184(3)
5.2.1 L × M Downsampling Algorithm (LMDS)
184(2)
5.2.2 L × M upsampling Algorithm (LMUS)
186(1)
5.3 Resizing with Arbitrary Factors
187(4)
5.4 Hybrid Resizing
191(2)
5.4.1 Computational Cost
192(1)
5.5 Summary
193(2)
6 Transcoding
195(40)
6.1 Intertransforms Conversion
196(12)
6.1.1 DWT to DCT
197(1)
6.1.1.1 Inserting Zeroes in DWT Coefficients
197(2)
6.1.1.2 DCT Domain Upsampling in 2D
199(1)
6.1.1.3 Upsampling the DCT for Multilevel DWT
200(1)
6.1.1.4 Wavelet Synthesis in the Compressed Domain
200(3)
6.1.1.5 Transcoding in 2-D
203(1)
6.1.2 DCT to DWT
204(1)
6.1.2.1 Even and Odd Downsampling of DCT Blocks
204(2)
6.1.2.2 Even and Odd Downsampling in 2-D
206(1)
6.1.2.3 Wavelet Analysis in the DCT Domain
206(1)
6.1.3 DCT to ICT
207(1)
6.1.4 ICT to DCT
208(1)
6.2 Image Transcoding: JPEG2000 to JPEG
208(3)
6.2.1 Transcoding with WBDT
209(1)
6.2.2 Transcoding with Wavelet Doubling
209(1)
6.2.3 Transcoding with DCT Domain Doubling
209(1)
6.2.4 Performance Metrics for Transcoding Schemes
210(1)
6.3 Video Downscaling
211(14)
6.3.1 Inverse Motion Compensation
215(1)
6.3.1.1 Single Blockwise Inverse Motion Compensation
215(2)
6.3.1.2 Macroblockwise Inverse Motion Compensation
217(1)
6.3.1.3 Video Downscaling and IMC: Integrated Scheme
218(2)
6.3.2 Motion Vector Refinement
220(1)
6.3.2.1 Adaptive Motion Vector Resampling (AMVR)
220(1)
6.3.2.2 Median Method
221(1)
6.3.2.3 Nonlinear Motion Vector Resampling (NLMR) Method
221(1)
6.3.3 Macroblock Type Declaration
222(1)
6.3.4 Downsizing MPEG2 Video
222(2)
6.3.5 Arbitrary Video Downsizing
224(1)
6.4 Frame Skipping
225(2)
6.5 Video Transcoding
227(4)
6.5.1 H.264 to MPEG-2
228(2)
6.5.1.1 Motion Estimation
230(1)
6.5.1.2 Skip Macroblock
230(1)
6.5.1.3 Intramacroblock
230(1)
6.5.1.4 Advantage of the Hybrid Approach
231(1)
6.5.2 MPEG-2 to H.264
231(1)
6.6 Error Resilient Transcoding
231(3)
6.7 Summary
234(1)
7 Image and Video Analysis
235(16)
7.1 Image and Video Editing
235(6)
7.1.1 Document Processing
236(2)
7.1.2 Caption Localization in a Video
238(1)
7.1.3 Shot Detection
239(2)
7.2 Object Recognition
241(2)
7.3 Image Registration
243(2)
7.4 Digital Watermarking
245(1)
7.5 Steganography
246(1)
7.6 Image and Video Indexing
247(3)
7.6.1 Image Indexing
247(1)
7.6.2 Video Indexing
248(1)
7.6.2.1 Key Frame Selection
248(1)
7.6.2.2 Key Video Object Plane Selection
249(1)
7.7 Summary
250(1)
Bibliography 251(14)
Index 265
Jayanta Mukhopadhyay is a professor and head of the Department of Computer Science and Engineering and the School of Information Technology at the Indian Institute of Technology in Kharagpur. He has held visiting positions at the University of California-Santa Barbara, the University of Southern California, and the National University of Singapore. He was also a Humboldt Research Fellow at the Technical University of Munich in 2002. Dr. Mukherjee is a senior member of the IEEE and a fellow of the Indian National Academy of Engineering. His research interests encompass image processing, pattern recognition, computer graphics, multimedia systems, and medical informatics.