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Spie Press Book

Digital Image Compression Techniques
Author(s): Majid Rabbani; Paul W. Jones
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Book Description

In order to utilize digital images effectively, specific techniques are needed to reduce the number of bits required for their representation. This Tutorial Text provides the groundwork for understanding these image compression tecniques and presents a number of different schemes that have proven useful. The algorithms discussed in this book are concerned mainly with the compression of still-frame, continuous-tone, monochrome and color images, but some of the techniques, such as arithmetic coding, have found widespread use in the compression of bilevel images. Both lossless (bit-preserving) and lossy techniques are considered. A detailed description of the compression algorithm proposed as the world standard (the JPEG baseline algorithm) is provided. The book contains approximately 30 pages of reconstructed and error images illustrating the effect of each compression technique on a consistent image set, thus allowing for a direct comparison of bit rates and reconstucted image quality. For each algorithm, issues such as quality vs. bit rate, implementation complexity, and susceptibility to channel errors are considered.

Book Details

Date Published: 1 February 1991
Pages: 240
ISBN: 9780819406484
Volume: TT07

Table of Contents
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Preface
Acknowledgments

Part I Background

1 Digital Images and Image Compression
1.1 Digital Image Formation
1.2 The Need for Image Compression
1.3 Classification of Compression Techniques
1.4 Effect of Digitization Parameters on Compression
1.5 Image Compression Standardization Activities
References

Part II Information Theory Concepts

2 Source Models and Entropy
2.1 Discrete Memoryless Sources
2.2 Extensions of a Discrete Memoryless Source
2.3 Markov Sources
2.3.1 Example
2.4 Extensions of a Markov Source and Adjoint Sources
2.4.1 Example
2.5 The Noiseless Source Coding Theorem
3 Variable-Length Codes
3.1 Code Efficiency and Source Extensions
3.2 Huffman Codes
3.3 Modified Huffman Codes
3.4 Limitations of Huffman Coding
3.5 Arithmetic Coding
3.5.1 The IBM Q-coder
4 Entropy Estimation and Lossless Compression
4.1 Structure and Entropy of the English Language
4.2 Predictability and Entropy of the English Language
4.3 Predictability and Entropy of Natural Images
5 Rate-Distortion Theory and Lossy Compression
References

Part III Lossless Compression Techniques

Introduction
6 Bit Plane Encoding
6.1 Gray Code
6.2 Runlength Encoding of Bit Planes
6.3 Arithmetic Encoding of Bit Planes
7 Lossless Predictive Coding
7.1 DPCM Predictor
7.2 Huffman Encoding of Differential Images
7.3 Arithmetic Encoding of Differential Images
8 Lossy Plus Lossless Residual Encoding
References

Part IV Lossy Compression Techniques

Introduction
9 Lossy Predictive Coding
9.1 Differential Pulse Code Modulation (DPCM)
9.1.1 Predictor optimization
9.1.2 Quantizer optimization
9.2 Adaptive DPCM
9.2.1 Adaptive prediction
9.2.2 Adaptive quantization
9.3 DPCM Results
9.4 Implementation Issues/Complexity of ADPCM
10 Transform Coding
10.1 Transforms as Coordinate Axes Rotations
10.2 Transforms as Basis Function Decompositions
10.3 Image Transforms
10.3.1 Karhunen-Lo�ve transform (KLT)
10.3.2 Discrete Fourier transform (DFT)
10.3.3 Discrete cosine transform (DCT)
10.3.4 Walsh-Hadamard transform (WHT)
10.4 Transform Coding Strategies
10.4.1 Zonal sample selection
10.4.2 Threshold sample selection
10.5 JPEG DCT Algorithm
10.5.1 JPEG baseline system
10.5.2 JPEG DCT example
10.6 JPEG DCT Results
10.7 Implementation Issues/Complexity of JPEG DCT
11 Block Truncation Coding
11.1 Quantizer Design
11.1.1 Moment-preserving quantizers
11.1.2 Error-minimizing quantizers
11.2 Source Coding of Bit Map and Reconstruction Levels
11.2.1 Reduced bit representation/joint quantization
11.2.2 Vector quantization encoding of reconstruction levels
11.2.3 Bit map omission
11.2.4 Independent/dependent bits
11.2.5 VQ encoding of bit map
11.3 Adaptive Block Size BTC
11.4 BTC Results
11.5 Implementation Issues/Complexity of Adaptive AMBTC
12 Vector Quantization
12.1 Codebook Generation
12.1.1 Linde-Buzo-Gray (LBG) algorithm
12.1.2 Codebook initialization
12.2 Codebook Design: Tree-Structured Codebooks
12.3 Codebook Design: Product Codes
12.4 Mean/Residual VQ (M/RVQ)
12.5 Interpolative/Residual VQ (I/RVQ)
12.6 Gain/Shape VQ (G/SVQ)
12.7 Classified VQ (CVQ)
12.8 Finite-State VQ (FSVQ)
12.9 VQ Results
12.10 Implementation/Complexity of M/RTVQ and I/RTVQ
13 Subband Coding
13.1 Analysis/Synthesis Filtering for 1-D Signals
13.2 Extension to 2-D Signals 1 3.3 Subband Coding Techniques
13.3.1 DPCM encoding
13.3.2 DPCM/PCM encoding
13.3.3 VQ encoding
13.4 Relationship Between Subband and Transform Coding
13.5 SBC Results
13.6 Implementation Issues/Complexity of SBC/VQ
14 Hierarchical Coding
14.1 Progressive Transmission
14.2 Multiuse Environments
14.3 Image Hierarchies
14.4 Fixed-Resolution Hierarchies
14.4.1 Bit planes
14.4.2 Tree-structured VQ
14.4.3 Transform-based hierarchical coding
14.5 Variable-Resolution Hierarchies
14.5.1 Subsampling pyramid
14.5.2 Mean pyramids
14.5.3 Knowlton�s technique
14.5.4 Prediction/residual pyramid
14.5.5 Hierarchical interpolation
14.5.6 Subband pyramid
15 Choosing a Lossy Compression Technique
15.1 Bit Rate/Quality Performance Summary
References
A Compression of Color Images
A.1 Statistical Spectral Compression
A.2 HVS Color Encoding
References |

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