Preface 1 Introduction 1.1 Compression Techniques 1.1.1 Lossless Compression 1.1.2 Lossy Compression 1.1.3 Measures of Performance 1.2 Modeling and Coding 1.3 Organization of This Book 1.4 Summary 1.5 Projects and Problems 2 Mathematical Preliminaries 2.1 Overview 2.2 A Brief Introduction to Information Theory 2.3 Models 2.3.1 Physical Models 2.3.2 Probability Models 2.3.3. Markov Models 2.3.4 Summary 2.5 Projects and Problems 3 Huffman Coding 3.1 Overview 3.2 "Good" Codes 3.3. The Huffman Coding Algorithm 3.3.1 Minimum Variance Huffman Codes 3.3.2 Length of Huffman Codes 3.3.3 Extended Huffman Codes 3.4 Nonbinary Huffman Codes 3.5 Adaptive Huffman Coding 3.5.1 Update Procedure 3.5.2 Encoding Procedure 3.5.3 Decoding Procedure 3.6 Applications of Huffman Coding 3.6.1 Lossless Image Compression 3.6.2 Text Compression 3.6.3 Audio Compression 3.7 Summary 3.8 Projects and Problems 4 Arithmetic Coding 4.1 Overview 4.2 Introduction 4.3 Coding a Sequence 4.3.1 Generating a Tag 4.3.2 Deciphering the Tag 4.4 Generating a Binary Code 4.4.1 Uniqueness and Efficiency of the Arithmetic Code 4.4.2 Algorithm Implementation 4.4.3 Integer Implementation 4.5 Comparison of Huffman and Arithmetic Coding 4.6 Applications 4.6.1 Bi-Level Image Compression-The JBIG Standard 4.6.2 Image Compression 4.7 Summary 4.8 Projects and Problems 5 Dictionary Techniques 5.1 Overview 5.2 Introduction 5.3 Static Dictionary 5.3.1 Diagram Coding 5.4 Adaptive Dictionary 5.4.1 The LZ77 Approach 5.4.2 The LZ78 Approach 5.5 Applications 5.5.1 File Compression-UNIX COMPRESS 5.5.2 Image Compression-the Graphics Interchange Format (GIF) 5.5.3 Compression over Modems-V.42 bis 5.6 Summary 5.7 Projects and Problems 6 Lossless Image Compression 6.1 Overview 6.2 Introduction 6.3 Facsimile Encoding 6.3.1 Run-Length Coding 6.3.2 CCITT Group 3 and 4-Recommendations T.4 and T.6 6.3.3 Comparison of MH, MR, MMR, and JBIG 6.4 Progressive Image Transmission 6.5 Other Image Compression Approaches 6.5.1 Linear Prediction Models 6.5.2 Context Models 6.5.3 Multiresolution Models 6.5.4 Modeling Prediction Errors 6.6 Summary 6.7 Projects and Problems 7 Mathematical Preliminaries 7.1 Overview 7.2 Introduction 7.3 Distortion Criteria 7.3.1 The Human Visual System 7.3.2 Auditory Perception 7.4 Information Theory Revisted 7.4.1 Conditional Entropy 7.4.2 Average Mutual Information 7.4.3 Differential Entropy 7.5 Rate Distortion Theory 7.6 Models 7.6.1 Probability Models 7.6.2 Linear System Models 7.6.3 Physical Models 7.7 Summary 7.8 Projects and Problems 8 Scalar Quantization 8.1 Overview 8.2 Introduction 8.3 The Quantization Problem 8.4 Uniform Quantizer 8.5 Adaptive Quantization 8.5.1 Forward Adaptive Quantization 8.5.2 Backward Adaptive Quantization 8.6 Nonuniform Quantization 8.6.1 pdf-Optimized Quantization 8.6.2 Companded Quantization 8.7 Entropy-Coded Quantization 8.7.1 Entropy Coding of Lloyd-Max Quantizer Outputs 8.7.2 Entropy-Constrained Quantization 8.7.3 High-Rate Optimum Quantization 8.8 Summary 8.9 Projects and Problems 9 Vector Quantization 9.1 Overview 9.2 Introduction 9.3 Advantages of Vector Quantization over Scalar Quantization 9.4 The Linde-Buzo-Gray Algorithm 9.4.1 Initializing the LBG Algorithm 9.4.2 The Empty Cell Problem 9.4.3 Use of LBG for Image Compression 9.5 Tree-Structured Vector Quantizers 9.5.1 Design of Tree-Structured Vector Quantizers 9.6 Structured Vector Quantizers 9.6.1 Pyramid Vector Quantization 9.6.2 Polar and Spherical Vector Quantizers 9.6.3 Lattice Vector Quantizers 9.7 Variations on the Theme 9.7.1 Gain-Shape Vector Quantization 9.7.2 Mean-Removed Vector Quantization 9.7.3 Classified Vector Quantization 9.7.4 Multistage Vector Quantization 9.7.5 Adaptive Vector Quantization 9.8 Summary 9.9 Projects and Problems 10 Differential Encoding 10.1 Overview 10.2 Introduction 10.3 The Basic Algorithm 10.4 Prediction in DPCM 10.5 Adaptive DPCM (ADPCM) 10.5.1 Adaptive Quantization in DPCM 10.5.2 Adaptive Prediction in DPCM 10.6 Delta Modulation 10.6.1 Constant Factor Adaptive Delta Modulation (CFDM) 10.6.2 Continuously Variable Slope Delta Modulation 10.7 Speech Coding 10.7.1 G.726 10.8 Summary 10.9 Projects and Problems 11 Subband Coding 11.1 Overview 11.2 Introduction 11.3 The Frequency Domain and Filtering 11.3.1 Filters 11.4 The Basic Subband Coding Algorithm 11.4.1 Bit Allocation 11.5 Application to Speech Coding-G.722 11.6 Application to Audio Coding-MPEG Audio 11.7 Application to Image Compression 11.7.1 Decomposing an Image 11.7.2 Coding the Subbands 11.8 Wavelets 11.8.1 Families of Wavelets 11.8.2 Wavelets and Image Compression 11.9 Summary 11.10 Projects and Problems 12 Transform Coding 12.1 Overview 12.2 Introduction 12.3 The Transform 12.4 Transforms of Interest 12.4.1 Karhunen-Loeve Transform 12.4.2 Discrete Cosine Transform 12.4.3 Discrete Sine Transform 12.4.4 Discrete Walsh-Hadamard Transform 12.5 Quantization and Coding of Transform Coefficients 12.6 Application to Image Compression-JPEG 12.6.1 The Transform 12.6.2 Quantization 12.6.3 Coding 12.7 Application to Audio Compression 12.8 Summary 12.9 Projects and Problems 13 Analysis/Synthesis Schemes 13.1 Overview 13.2 Introduction 13.3 Speech Compression 13.3.1 The Channel Vocoder 13.3.2 The Linear Predictive Coder (Gov.Std.LPC-10) 13.3.3 Code Excited Linear Prediction (CELP) 13.3.4 Sinusoidal Coders 13.4 Image Compression 13.4.1 Fractal Compression 13.5 Summary 13.6 Projects and Problems 14 Video Compression 14.1 Overview 14.2 Introduction 14.3 Motion Compensation 14.4 Video Signal Representation 14.5 Algorithms for Videoconferencing and Videophones 14.5.1 ITU_T Recommendation H.261 14.5.2 Model-Based Coding 14.6 Asymmetric Applications 14.6.1 The MPEG Video Standard 14.7 Packet Video 14.7.1 ATM Networks 14.7.2 Compression Issues in ATM Networks 14.7.3 Compression Algorithms for Packet Video 14.8 Summary 14.9 Projects and Problems A Probability and Random Processes A.1 Probability A.2 Random Variables A.3 Distribution Functions A.4 Expectation A.5 Types of Distribution A.6 Stochastic Process A.7 Projects and Problems B A Brief Review of Matrix Concepts B.1 A Matrix B.2 Matrix Operations C Codes for Facsimile Encoding D The Root Lattices Bibliography Index

Introduction to data compression

Optical constant spectra for silicon and thermally grown silicon dioxide have been simultaneously determined using variable angle of incidence spectroscopic ellipsometry from 0.75 to 6.5 eV. Spectroscopic ellipsometric data sets acquired at multiple angles of incidence from seven samples with oxide thicknesses from 2 to 350 nm were analyzed using a self-contained multi-sample technique to obtain Kramers–Kronig consistent optical constant spectra. The investigation used a systematic approach utilizing optical models of increasing complexity in order to investigate the need for fitting the thermal SiO2 optical constants and including an interface layer between the silicon and SiO2 in modeling the data. A detailed study was made of parameter correlation effects involving the optical constants used for the interface layer. The resulting thermal silicon dioxide optical constants were shown to be independent of the precise substrate model used, and were found to be approximately 0.4% higher in index than publis...

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Ellipsometric determination of optical constants for silicon and thermally grown silicon dioxide via a multi-sample, multi-wavelength, multi-angle investigation

In a typical reflection ellipsometry experiment, one characterizes the polarization state change that a polarized light beam undergoes upon reflection from a specular surface. This measurement provides ρ≡rp/rs, where rp and rs are the complex amplitude reflection coefficients of the surface for p‐ and s‐polarized waves. Nearly 15 years have passed since the development of automatic ellipsometers along with the detailed calibration, error analysis, and data reduction procedures to be used with them. More specifically, these powerful instruments permit (1) determination of bulk dielectric functions and nondestructive depth profiling of static multilayered materials through measurements as a function of photon energy and (2) characterization of dynamic surfaces in adverse environments through measurements as a function of time at fixed photon energy. In the 15 intervening years, the major research thrusts in ellipsometry have been the exploitation of these instruments in materials and process characterizatio...

Automatic rotating element ellipsometers: Calibration, operation, and real‐time applications

Measurement of the Anisotropic Refractive Indices of Spin Cast Thin Poly(2‐methoxy‐5‐(2′‐ethyl‐hexyloxy)‐p‐phenylenevinylene) (MEH–PPV) Films

We have designed and constructed a new type of spectroscopic ellipsometer to study the optical properties of materials in the 3500-8000-A wavelength range. In the system, the analyzer and polarizer are driven 10(4) steps/revolution by two stepping motors that have hollow shafts and rotate synchronously with a speed ratio of 2:1, i.e., A = 2P. Both the polarizer and analyzer are mounted directly on the shafts to avoid mechanical transmission and vibration problems entirely and make the system simple and reliable. An additional source polarizer was placed in the optical path to reduce the slight polarization effects of the light source. The light intensity finally received by the detector contained five components, one dc and four ac, with frequencies of ω(0), 2ω(0), 3ω(0), and 4ω(0), respectively. One can independently obtain the ellipsometric parameters of ψ and Δ as well as the optical constants by calculating any one of the two sets of ac signals, with a raw data self-consistency of better than 0.5%. The incident angle, aligned precisely by a laser beam, was continuously variable through a mechanical system with a computercontrolled resolution of 0.001° or a visual resolution of 0.005°. The system operations, including data acquisition and reduction, high-voltage control of the photomultiplier, incident angle, as well as wavelength setting and scanning, were fully and automatically controlled by a 386-based microcomputer. We self-calibrated the system by adjusting and setting precisely the initial azimuthal angles of the prisms. The results from the measured spectra of the complex refractive index for a gold-film sample are presented, and we show that the data obtained at three different incident angles of 65°, 70°, and 75° are remarkably consistent with one another. A comparison of the two results from the ellipsometry and reflectance measurements is given. The experimental skills and system error reduction are discussed in detail.

Design of a scanning ellipsometer by synchronous rotation of the polarizer and analyzer.

The sensitivity of spectroscopic ellipsometry data to multilayer model parameters is shown to be a strong function of the angle of incidence. A quantitative study of sensitivity versus angle of incidence is performed for a GaAs-Al(x)Ga(1-x)As-GaAs substrate structure, showing that maximum sensitivity to layer thicknesses and AlGaAs composition occurs near the wavelength-dependent principal angle. These results are verified by experimental measurements on two molecular-beam epitaxy grown samples.

Variable angle of incidence spectroscopic ellipsometry: Application to GaAs‐AlxGa1−xAs multiple heterostructures

Variable angle spectroscopic ellipsometry: A non-destructive characterization technique for ultrathin and multilayer materials☆

In the most commonly used form of ellipsometry, a monochromatic collimated linearly polarized light beam is directed at an angle φ to the normal of a sample under study. The specularly reflected beam is, in general, elliptically polarized, and the state of polarization is analyzed using a second polarizer and photodetector.1 Figure 1 shows a schematic of the rotating analyzer automated spectroscopic ellipsometer used at the University of Nebraska. The angle of incidence can be set over a wide range of angles, with a precision and repeatability of ±0.01 angular degrees. A computer controls the monochromator, the azimuth of a stepper motor driven polarizer, a shutter, and the digitization of the detector signal. There are several other schemes used for acquiring ellipsometric data, and these are discussed in several sources.

Variable Angle Spectroscopic Ellipsometry (VASE) for the Study of Ion-Beam and Growth-Modified Solids

Multiple angle of incidence spectroscopic ellipsometric data show that implantation of 150‐keV molybdenum ions into polished molybdenum laser mirrors causes microscopic surface smoothing, and that most of the microscopic roughness is removed by a fluence of 5×1015 cm−2. Implantation of Au at 1 MeV significantly increases the microscopic roughness, and also changes the bulk optical properties. 3‐MeV Ni ion implantation causes only small changes in the surface and bulk properties. A dielectric film, probably a hydrocarbon, is found to condense on the mirrors in a laboratory atmosphere.

Study of Mo-, Au-, and Ni-implanted molybdenum laser mirrors by multiple angle of incidence spectroscopic ellipsometry

Carbon fibers have been grown from methane gas, on iron seeded substrates at 1370 K, and subsequently annealed to a series of temperatures between 2800 K and 3475 K. The subsequent measurement of the electrical resistivity are reported as a function of temperature from 4 K to 2100 K. The two band model for the resistivity of graphite is well fit to the data, and results in physically reasonable parameters of the fits. The resistivity vs temperature results for two boron doped fibers are reported as well. The product of the resistivity times density for these fibers is lower than that product for any refractory metal, for temperatures above about 1000 K, suggesting the usefulness of these fibers as high temperature materials in space applications.

Martin C. Rost

Papers

Variable angle of incidence spectroscopic ellipsometry: Application to GaAs‐AlxGa1−xAs multiple heterostructures

Variable angle spectroscopic ellipsometry: A non-destructive characterization technique for ultrathin and multilayer materials☆

Variable Angle Spectroscopic Ellipsometry (VASE) for the Study of Ion-Beam and Growth-Modified Solids

Study of Mo-, Au-, and Ni-implanted molybdenum laser mirrors by multiple angle of incidence spectroscopic ellipsometry

Electrical resistivity (4K to 2100K) of annealed vapor growth carbon fibers