The Transform and Data Compression Handbook

By: Kamisetty Ramam Rao (Editor), Patrick C. Yip (Editor)

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Hardcover | 27 September 2000 | Edition Number 1

At a Glance

Hardcover
408 Pages

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24.2 x 16.2 x 3.0

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$777.75

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Data compression is one of the main contributing factors in the explosive growth in information technology. Without it, a number of consumer and commercial products, such as DVD, videophone, digital camera, MP3, video-streaming and wireless PCS, would have been virtually impossible. Transforming the data to a frequency or other domain enables even more efficient compression. By illustrating this intimate link, The Transform and Data Compression Handbook serves as a much-needed handbook for a wide range of researchers and engineers.

The authors describe various discrete transforms and their applications in different disciplines. They cover techniques, such as adaptive quantization and entropy coding, that result in significant reduction in bit rates when applied to the transform coefficients. With clear and concise presentations of the ideas and concepts, as well as detailed descriptions of the algorithms, the authors provide important insight into the applications and their limitations. Data compression is an essential step towards the efficient storage and transmission of information. The Transform and Data Compression Handbook provides a wealth of information regarding different discrete transforms and demonstrates their power and practicality in data compression.

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Non-Fiction Computing & I.T.Business Applications Word Processing Software Information Technology General Issue Databases Data Capture & Analysis Computer Networking & Communications Engineering & Technology Electronics & Communications Engineering Communications Engineering & Telecommunications Computer Science

Systems Analysis & Design Energy Technology & Engineering Electrical Engineering

Karhunen-Loeve Transform	p. 1
Introduction	p. 1
Data Decorrelation	p. 2
Calculation of the KLT	p. 9
Performance of Transforms	p. 11
Information Theory	p. 11
Quantization	p. 13
Truncation Error	p. 13
Block Size	p. 15
Examples	p. 17
Calculation of KLT	p. 17
Quantization and Encoding	p. 18
Generalization	p. 22
Markov-1 Solution	p. 24
Medical Imaging	p. 25
Color Images	p. 28
Summary	p. 30
References	p. 34
The Discrete Fourier Transform	p. 37
Introduction	p. 37
The DFT Matrix	p. 39
An Example	p. 40
DFT Frequency Analysis	p. 41
Selected Properties of the DFT	p. 45
Symmetry Properties	p. 47
Real-Valued DFT-Based Transforms	p. 49
The Fast Fourier Transform	p. 55
The DFT in Coding Applications	p. 58
The DFT and Filter Banks	p. 60
Cosine-Modulated Filter Banks	p. 63
Complex DFT-Based Filter Banks	p. 66
Conclusion	p. 68
FFT Web sites	p. 72
References	p. 74
Comparametric Transforms for Transmitting Eye Tap Video with Picture Transfer Protocol (PTP)	p. 79
Introduction: Wearable Cybernetics	p. 79
Historical Overview of WearComp	p. 80
Eye Tap Video	p. 80
The Edgertonian Image Sequence	p. 81
Edgertonian versus Nyquist Thinking	p. 81
Frames versus Rows, Columns, and Pixels	p. 82
Picture Transfer Protocol (PTP)	p. 83
Best Case Imaging and Fear of Functionality	p. 84
Comparametric Image Sequence Analysis	p. 88
Camera, Eye, or Head Motion: Common Assumptions and Terminology	p. 91
VideoOrbits	p. 92
Framework: Comparameter Estimation and Optical Flow	p. 94
Feature-Based Methods	p. 94
Featureless Methods Based on Generalized Cross-Correlation	p. 95
Featureless Methods Based on Spatio-Temporal Derivatives	p. 96
Multiscale Projective Flow Comparameter Estimation	p. 99
Four Point Method for Relating Approximate Model to Exact Model	p. 101
Overview of the New Projective Flow Algorithm	p. 102
Multiscale Repetitive Implementation	p. 103
Exploiting Commutativity for Parameter Estimation	p. 104
Performance/Applications	p. 106
A Paradigm Reversal in Resolution Enhancement	p. 106
Increasing Resolution in the "Pixel Sense"	p. 107
Summary	p. 109
Acknowledgements	p. 111
References	p. 112
Discrete Cosine and Sine Transforms	p. 117
Introduction	p. 117
The Family of DCTs and DSTs	p. 118
Definitions of DCTs and DSTs	p. 118
Mathematical Properties	p. 119
Relations to the KLT	p. 121
A Unified Fast Computation of DCTs and DSTs	p. 122
Definitions of Even-Odd Matrices	p. 123
DCT-II/DST-II and DCT-III/DST-III Computation	p. 124
DCT-I and DST-I Computation	p. 129
DCT-IV/DST-IV Computation	p. 131
Implementation of the Unified Fast Computation of DCTs and DSTs	p. 134
The 2-D DCT/DST Universal Computational Structure	p. 146
The Fast Direct 2-D DCT/DST Computation	p. 146
Implementation of the Direct 2-D DCT/DST Computation	p. 152
DCT and Data Compression	p. 161
DCT-Based Image Compression/Decompression	p. 162
Data Structures for Compression/Decompression	p. 166
Setting the Quantization Table	p. 168
Standard Huffman Coding/Decoding Tables	p. 170
Compression of One Sub-Image Block	p. 172
Decompression of One Sub-Image Block	p. 179
Image Compression/Decompression	p. 184
Compression of Color Images	p. 186
Results of Image Compression	p. 188
Summary	p. 191
References	p. 192
Lapped Transforms for Image Compression	p. 197
Introduction	p. 197
Notation	p. 198
Brief History	p. 198
Block Transforms	p. 199
Factorization of Discrete Transforms	p. 200
Discrete MIMO Linear Systems	p. 201
Block Transform as a MIMO System	p. 203
Lapped Transforms	p. 204
Orthogonal Lapped Transforms	p. 204
Nonorthogonal Lapped Transforms	p. 210
LTs as MIMO Systems	p. 210
Factorization of Lapped Transforms	p. 213
Hierarchical Connection of LTs: An Introduction	p. 215
Time-Frequency Diagram	p. 215
Tree-Structured Hierarchical Lapped Transforms	p. 217
Variable-Length LTs	p. 219
Practical Symmetric LTs	p. 222
The Lapped Orthogonal Transform: LOT	p. 222
The Lapped Bi-Orthogonal Transform: LBT	p. 223
The Generalized LOT: GenLOT	p. 226
The General Factorization: GLBT	p. 230
The Fast Lapped Transform: FLT	p. 233
Modulated LTs	p. 236
Finite-Length Signals	p. 240
Overall Transform	p. 241
Recovering Distorted Samples	p. 243
Symmetric Extensions	p. 244
Design Issues for Compression	p. 246
Transform-Based Image Compression Systems	p. 248
JPEG	p. 249
Embedded Zerotree Coding	p. 250
Other Coders	p. 252
Performance Analysis	p. 253
JPEG	p. 253
Embedded Zerotree Coding	p. 255
Conclusions	p. 258
References	p. 260
Wavelet-Based Image Compression	p. 267
Introduction	p. 267
Dyadic Wavelet Transform	p. 268
Two-Channel Perfect-Reconstruction Filter Bank	p. 270
Dyadic Wavelet Transform, Multiresolution Representation	p. 272
Wavelet Smoothness	p. 273
Wavelet-Based Image Compression	p. 274
Lossy Compression	p. 274
EZW Algorithm	p. 278
SPIHT Algorithm	p. 285
WDR Algorithm	p. 294
ASWDR Algorithm	p. 299
Lossless Compression	p. 305
Color Images	p. 305
Other Compression Algorithms	p. 306
Ringing Artifacts and Postprocessing Algorithms	p. 306
References	p. 306
Fractal-Based Image and Video Compression	p. 313
Introduction	p. 313
Basic Properties of Fractals and Image Compression	p. 314
Contractive Affine Transforms, Iterated Function Systems, and Image Generation	p. 316
Image Compression Directly Based on the IFS Theory	p. 318
Image Compression Based on IFS Library	p. 321
Image Compression Based on Partitioned IFS	p. 322
Image Partitions	p. 323
Distortion Measure	p. 323
A Class of Discrete Image Transformations	p. 324
Encoding and Decoding Procedures	p. 325
Experimental Results	p. 326
Image Coding Using Quadtree Partitioned IFS (QPIFS)	p. 326
RMS Tolerance Selection	p. 328
A Compact Storage Scheme	p. 329
Experimental Results	p. 331
Image Coding by Exploiting Scalability of Fractals	p. 333
Image Spatial Sub-Sampling	p. 334
Decoding to a Larger Image	p. 334
Experimental Results	p. 334
Video Sequence Compression using Quadtree PIFS	p. 336
Definitions of Types of Range Blocks	p. 336
Encoding and Decoding Processes	p. 338
Storage Requirements	p. 340
Experimental Results	p. 340
Discussion	p. 341
Other Fractal-Based Image Compression Techniques	p. 341
Segmentation-Based Coding Using Fractal Dimension	p. 341
Yardstick Coding	p. 342
Conclusions	p. 343
References	p. 343
Compression of Wavelet Transform Coefficients	p. 347
Introduction	p. 347
Embedded Coefficient Coding	p. 353
Statistical Context Modeling of Embedded Bit Stream	p. 357
Context Dilution Problem	p. 359
Context Formation	p. 360
Context Quantization	p. 362
Optimization of Context Quantization	p. 365
Dynamic Programming for Minimum Conditional Entropy	p. 367
Fast Algorithms for High-Order Context Modeling	p. 369
Context Formation via Convolution	p. 370
Shared Modeling Context for Signs and Textures	p. 371
Experimental Results	p. 373
Lossy Case	p. 373
Lossless Case	p. 374
Summary	p. 374
References	p. 375
Index	p. 379
Table of Contents provided by Syndetics. All Rights Reserved.

The Transform and Data Compression Handbook

At a Glance

Hardcover

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How to return your order

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