Handbook of Visual Communications (eBook)

Hseuh-Ming Hang, John W. Woods (Herausgeber)

eBook Download: PDF | EPUB

2012 | 1. Auflage
518 Seiten
Elsevier Science (Verlag)
978-0-08-091854-9 (ISBN)

Infos

This volume is the most comprehensive reference work on visual communications to date. An international group of well-known experts in the field provide up-to-date and in-depth contributions on topics such as fundamental theory, international standards for industrial applications, high definition television, optical communications networks, and VLSI design. The book includes information for learning about both the fundamentals of image/video compression as well as more advanced topics in visual communications research. In addition, the Handbook of Visual Communications explores the latest developments in the field, such as model-based image coding, and provides readers with insight into possible future developments. - Displays comprehensive coverage from fundamental theory to international standards and VLSI design - Includes 518 pages of contributions from well-known experts - Presents state-of-the-art knowledge--the most up-to-date and accurate information on various topics in the field - Provides an extensive overview of international standards for industrial applications

Inhalt

Front Cover 1
Handbook of Visual Communications 4
Copyright Page 5
Table of Contents 6
Contributors 12
Preface 14
CHAPTER 1. VIDEO DATA COMPRESSION 16
1.1 Introduction 16
1.2 Waveform Encoding 22
1.3 Parameter Coding 25
CHAPTER 2. INFORMATION THEORY AND IMAGE CODING 28
2.1 Introduction 29
2.2 Noiseless Source Coding 29
2.3 Continuous-Amplitude Sources 33
2.4 Scalar Quantization 40
2.5 Vector Coding 49
2.6 Transform Coding 56
2.7 Predictive Coding 69
2.8 Subband Coding 72
2.9 Conclusions 83
References 84
CHAPTER 3. IMAGE COMPRESSION BASED ON MODELS OF HUMAN VISION 88
3.1 Introduction 89
3.2 Overview of Signal Compression 91
3.3 Visual Signal, Human Perception, and Time-Frequency Analysis 101
3.4 Filterbanks and Transforms in Image Processing 106
3.5 Quantization 112
3.6 Perceptual Image Coding 119
3.7 Perceptual Coding of Video 132
3.8 Research Directions 135
References 137
CHAPTER 4. BILEVEL IMAGE CODING 142
4.1 Introduction 142
4.2 Compressed Rasters versus Page Description Language 144
4.3 Group 3 and Group 4 Coding 148
4.4 Joint Bilevel Imaging Group Coding 153
4.5 Conclusions 160
References 161
CHAPTER 5. MOTION ESTIMATION FOR IMAGE SEQUENCE COMPRESSION 162
5.1 Introduction 163
5.2 Motion Estimation and Compensation 164
5.3 Block Matching Method 170
5.4 Differential Method 179
5.5 Fourier Method 189
5.6 Concluding Remarks 196
References 198
CHAPTER 6. VECTOR QUANTIZATION TECHNIQUES IN IMAGE COMPRESSION 204
6.1 Introduction 205
6.2 Vector Quantization with Memory 212
6.3 Adaptive Vector Quantization 217
6.4 Vector Quantization in Transform and Subband Coding 220
6.5 Vector Quantization in Interframe Video Coding 223
6.6 Variable Bit-Rate Vector Quantization 225
6.7 Enhanced Decoding 230
6.8 Concluding Remarks 234
References 235
CHAPTER 7. TRANSFORM CODING 238
7.1 Introduction 238
7.2 Transforming the Signal 241
7.3 Performance of Transforms 246
7.4 Representation of a Transformed Image 246
7.5 Quantizers and Entropy Coding 248
7.6 Quantizer Selection 251
7.7 Human Visual Sensitivity Weighting 253
7.8 Transform Coders: Zonal Sampling 255
7.9 Joint Pictures Experts Group Baseline System 257
7.10 Interframe Image Coding 261
7.11 Vector Quantization 272
7.12 Conclusions 273
Appendix 7.A: Discrete Cosine Transform 273
Appendix 7.B: Lapped Orthogonal Transform 274
References 276
CHAPTER 8. SUBBAND AND WAVE LETFILTERS FOR HIGH-DEFINITION VIDEO COMPRESSION 280
8.1 Introduction 281
8.2 Review of Subband Filter Sets 284
8.3 Power Spectral Densities 299
8.4 Noise in a Subband Synthesis System 302
8.5 Bit Allocation Algorithm 303
8.6 Description of the Encoder 304
8.7 Results 305
8.8 Conclusions 309
Appendix 8.A: Subband Filter Coefficients 309
References 312
CHAPTER 9. HIERARCHICAL CODING 314
9.1 Introduction 314
9.2 Compatible Coding 317
9.3 Infraframe Hierarchical Source Coding 322
9.4 Hierarchical Channel Coding for Asynchronous Transfer Mode 341
9.5 Experimental Results 346
9.6 Discussion 352
References 352
CHAPTER 10. MODEL-BASED CODING 356
10.1 Introduction 356
10.2 Model-Based Approaches to Image Coding 357
10.3 A General Description of Three-Dimensional Model-Based Coding 360
10.4 An Example of a Three-Dimensional Model-Based Coding for a Person's Face 365
10.5 Model-Based/Waveform Hybrid Coding 368
10.6 Applications and Implementations 372
10.7 Remaining Problems for Three-Dimensional Model-Based Coding 375
References 376
CHAPTER 11. IMAGE AND VIDEO CODING STANDARDS 380
11.1 Introduction 380
11.2 IPEG Still-Color Image Coding 381
11.3 Videoconferencing Standards H.261 390
11.4 Moving Picture Experts Group 399
11.5 Conclusion 406
References 407
CHAPTER 12. HYBRID HIGH-DEFINITION TELEVISION 408
12.1 Definition and Standard for High-Definition Television 409
12.2 Basic Construction of a Hybrid High-Definition Television System 411
12.3 Types of Hybrid Systems 412
12.4 Multidimensional Sampling 413
12.5 Subsampling System 414
12.6 Sampled Value Transmission 417
12.7 Adaptive Subsampling 418
12.8 System Requirements 419
12.9 Compatibility 421
12.10 Commonality with the Existing System 423
12.11 Concepts of MUSE and HD-MAC 423
12.12 MUSE System 424
12.13 HD-MAC System 429
References 435
CHAPTER 13. VIDEO COMMUNICATIONS TECHNOLOGIES I: NARROWBAND TRANSMISSIONS 436
13.1 Introduction 436
13.2 Wireline Loop Transmission 438
13.3 Wireless Radio Transmission 448
13.4 Conclusion 459
References 459
CHAPTER 14. VIDEO COMMUNICATIONS TECHNOLOGIES II: BROADBAND CABLE TELEVISION TRANSMISSIONS 462
14.1 Introduction 462
14.2 Coaxial Cable Distribution of Video Signals 463
14.3 From Coaxial to Optical Fiber Cable Television: Current Status 467
14.4 Optical Fiber Distribution of Video Signals 469
14.5 Conclusion 476
References 477
CHAPTER 15. VLSI FOR VIDEO CODING 480
15.1 Introduction 480
15.2 Required Parallelism of Video Coding Algorithms 484
15.3 Key Components for Function-Oriented Implementations 491
15.4 Programmable Multiprocessor Systems 505
15.5 Conclusion 512
References 513
Index 516

Textauszug

Chapter 1

Video Data Compression

B.G. Haskell    Visual Communications Research Department, AT&T Bell Laboratories, Holmdel, New Jersey

1.1 Introduction

A considerable effort has been underway for some time to develop inexpensive transmission techniques that take advantage of recent advances in electronic technology as well as expected future developments. Most of the attention has been focused on digital systems because, as is well known, noise does not accumulate in digital regenerators as it does in analog amplifiers and, in addition, signal processing is much easier in a digital format.

Progress is being made on two fronts. First, the present high cost per bit of transmitting a digital data stream has generated interest in a number of methods that are currently being evaluated for cost reduction. While these methods have general applications and are not confined to a data stream produced by a video signal source, it is important to remember that video bit rates tend to be considerably higher than those required for voice or data transmission. The most promising techniques for more economical digital transmission include optical fibers, digital satellite, broadband ISDN, and digital transmission over the air, among others.

The second front on which progress is being made involves reducing the number of bits that have to be transmitted in a video communication system. Bit-rate reduction is accomplished by eliminating, as much as possible, the substantial amount of redundant information that exists in a video signal as it leaves the camera. The amount of signal processing required to reduce the redundancy determines the economic feasibility of using this method in a given system. The savings that accrue from lowering the transmission bit rate must more than offset the cost of the required signal processing if redundancy reduction is to be economical.

Present costs of digital logic and digital memory are low enough to make this type of signal processing economically very attractive for use in long distance videoconferencing links over existing facilities. Furthermore, it is expected that the cost of digital logic and memory will continue to decline. Therefore, it is conjectured by those knowledgeable in the field that signal processing for bit-rate reduction will have an important part to play in all video systems, and in many cases, it could become the overriding factor determining economic feasibility.

To transmit video information at the minimum bit rate for a given quality of reproduction, it is necessary to exploit our understanding of many branches of science. Ideally the engineer should have an appreciation of motion pictures, colorimetry, human vision, signal theory, display devices, and so on. As might be expected any individual can have only a smattering of knowledge on such a diverse range of topics, and a specialist in any one topic will readily confess to a certain amount of ignorance even in his or her chosen field. As engineers we are concerned with complex stimuli and their human perception, as well as the final utilization of the perceived information. Knowledge of these is often unavailable or sketchy, forcing us to design encoders based on a relatively primitive understanding of the problem. The limits of bit-rate compression will be approached, we believe, only as our knowledge of stimuli, perception, and utilization increases.

Thus, in opening a discussion of video bit-rate compression we are very aware of our own limitations. Our modest objective of defining the state of the art is, we are well aware, open to the criticisms of oversimplification, serious omissions, and factual disagreement. As for where the subject is heading and its inherent limitations, we confess myopia and will not be surprised by a discovery that could not have been extrapolated from existing thinking and known ignorances. But first let us set the stage for our discussion. The conventional representation of a digital communication link for the transmission of audio or pictorial information is shown in Fig. 1.1. The function of the source encoder is to operate on an analog of audio or pictures, x(t), and to convert it into a stream of binary digits, s(t). The source decoder at the receiver accepts a binary signal S(t) and produces a continuous signal X(t). It may not be necessary to ensure X(t) = x(t), but what does matter is that after transduction, e.g., loudspeaker or TV tube, X(t) should be perceived as x(t), subject to an acceptable quality criterion. Although x(t) does not always have to be identical to X(t), system engineers prefer s(t) = S(t); i.e., the channel appears ideal. Most practical channels contain dispersion, nonlinearities, additive noise, multipath fading, interference from other channels, and so on. These imperfections are overcome largely by preprocessing and postprocessing the binary signals s(t) and S(t) by the channel codec and terminal equipment. The transmitting terminal equipment operates on c(t) to produce (perhaps by conversion to multilevel, modulation, filtering, etc.) a signal f(t) that is suitable for combating the imperfections of the communication channel. The signal F(t) that emerges from the channel may differ considerably from f(t). After demodulation, a binary signal C(t) is regenerated using adaptive equalization of the channel and adaptive detection strategies. The binary signal C(t) is then channel and source decoded to produce S(t).

Figure 1.1 Digital communication link for the transmission of audio or pictorial information.

The purpose of this book is to discuss mostly source encoding. However, Fig. 1.1 demonstrates that S(t) is dependent on the channel terminal equipment, the channel codec, and of course, the channel. Thus, encoding picture signals is not merely a source encoding problem, but may include the complete communication system. For example, if the channel is known to result in a high bit error rate (ber), then the effect on the recovered signal X(t) may be mitigated by altering the modulation and regeneration strategies, increasing the length of the check bits in the channel coding words, altering the source encoding algorithm, or combinations of all of these. The conventional arrangement of source and channel codecs may be altered, even merged. Postprocessing of X(t) can also be successfully employed.

Thus, we are interested in the source codec, its algorithms, how they relate to the signals it encodes, how the bit rate can be reduced by exploiting the source signal statistics and properties of human perception, the variety of quality criteria, the codec complexity, and above all, how these phenomena are interrelated and can be traded to approach an optimum design.

We therefore present a discussion of picture sources and our scant knowledge of the salient properties of human perception. Armed with this we describe the current state of the art in waveform and parameter coding and conclude with directions for the future, guessing at where we believe some ultimate limitations may be found.

1.1.1 Picture Sources

Video processing or transmission systems typically start with a two-dimensional distribution of light intensity. Thus, three-dimensional scenes must first be projected onto a two-dimensional plane by an optical imaging system. Color pictures can usually be represented by three such light intensity distributions in three primary bands of wavelengths. If moving objects are to be accommodated, the light intensity must change with time.

The two-dimensional light intensity distribution is then usually raster scanned to produce a one-dimensional waveform. Facsimile involves single pictures, while in television the scene is repetitively raster scanned (usually with interlace to avoid flicker). Black/white pictures, e.g., printed or handwritten text, line drawings, weather maps, produce a two-level or binary waveform.

Color pictures produce three such waveforms corresponding to the three primaries. These are then usually converted by linear combination into a luminance (monochrome brightness) component and two chrominance (hue and saturation) components. Multiplexing methods for further combining these components into a single composite waveform are well known and widely used; however, the luminance component usually takes up most of the channel capacity.

1.1.2 The Eye and Seeing

The eye is the organ of sight, having at its rear an inner nervous coating known as the retina. Rays of light pass through the cornea, aqueous humor, lens, and vitreous body to form an image on the retina. The central area of the retina, known as the fovea, provides high resolution and good color vision in about 1 degree of solid angle. The images on the retinas are sent along two optic nerves, one for each eye, until they meet at the optic chiasma, where half the fibers of each nerve diverge to opposite sides of the brain. This enables observations in three dimensions.

The eye behaves as a two-dimensional low-pass filter for...

Produkt-Details

• Erscheint lt. Verlag: 2.12.2012
• Sprache: englisch
• Themenwelt: Informatik ► Grafik / Design ► Digitale Bildverarbeitung
• Themenwelt: Informatik ► Grafik / Design ► Film- / Video-Bearbeitung
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Nachrichtentechnik
• ISBN-10: 0-08-091854-9 / 0080918549
• ISBN-13: 978-0-08-091854-9 / 9780080918549