Tools for signal compression
This book presents tools and algorithms required to compress/uncompress signals such as speech and music. These algorithms are largely used in mobile phones, DVD players, HDTV sets, etc. In a first rather theoretical part, this book presents the standard tools used in compression systems: scalar an...
| Autor principal: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken, N.J. :
ISTE ; John Wiley & Sons
c2011.
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| Edición: | 1st edition |
| Colección: | ISTE publications.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628202506719 |
Tabla de Contenidos:
- Cover; Tools for Signal Compression; Title Page; Copyright Page; Table of Contents; Introduction; PART 1. TOOLS FOR SIGNAL COMPRESSION; Chapter 1. Scalar Quantization; 1.1. Introduction; 1.2. Optimumscalar quantization; 1.2.1. Necessary conditions for optimization; 1.2.2. Quantization error power; 1.2.3. Further information; 1.2.3.1. Lloyd-Max algorithm; 1.2.3.2. Non-linear transformation; 1.2.3.3. Scale factor; 1.3. Predictive scalar quantization; 1.3.1. Principle; 1.3.2. Reminders on the theory of linear prediction; 1.3.2.1. Introduction: least squares minimization
- 1.3.2.2. Theoretical approach1.3.2.3. Comparing the two approaches; 1.3.2.4. Whitening filter; 1.3.2.5. Levinson algorithm; 1.3.3. Prediction gain; 1.3.3.1. Definition; 1.3.4. Asymptotic value of the prediction gain; 1.3.5. Closed-loop predictive scalar quantization; Chapter 2. Vector Quantization; 2.1. Introduction; 2.2. Rationale; 2.3. Optimum codebook generation; 2.4. Optimum quantizer performance; 2.5. Using the quantizer; 2.5.1. Tree-structured vector quantization; 2.5.2. Cartesian product vector quantization; 2.5.3. Gain-shape vector quantization; 2.5.4. Multistage vector quantization
- 2.5.5. Vector quantization by transform2.5.6. Algebraic vector quantization; 2.6. Gain-shape vector quantization; 2.6.1. Nearest neighbor rule; 2.6.2. Lloyd-Max algorithm; Chapter 3. Sub-band Transform Coding; 3.1. Introduction; 3.2. Equivalence of filter banks and transforms; 3.3. Bit allocation; 3.3.1. Defining the problem; 3.3.2. Optimum bit allocation; 3.3.3. Practical algorithm; 3.3.4. Further information; 3.4. Optimum transform; 3.5. Performance; 3.5.1. Transform gain; 3.5.2. Simulation results; Chapter 4. Entropy Coding; 4.1. Introduction
- 4.2. Noiseless coding of discrete, memoryless sources4.2.1. Entropy of a source; 4.2.2. Coding a source; 4.2.2.1. Definitions; 4.2.2.2. Uniquely decodable instantaneous code; 4.2.2.3. Kraft inequality; 4.2.2.4. Optimal code; 4.2.3. Theorem of noiseless coding of a memoryless discrete source; 4.2.3.1. Proposition 1; 4.2.3.2. Proposition 2; 4.2.3.3. Proposition 3; 4.2.3.4. Theorem; 4.2.4. Constructing a code; 4.2.4.1. Shannon code; 4.2.4.2. Huffman algorithm; 4.2.4.3. Example 1; 4.2.5. Generalization; 4.2.5.1. Theorem; 4.2.5.2. Example 2; 4.2.6. Arithmetic coding
- 4.3. Noiseless coding of a discrete source with memory4.3.1. New definitions; 4.3.2. Theorem of noiseless coding of a discrete source with memory; 4.3.3. Example of a Markov source; 4.3.3.1. General details; 4.3.3.2. Example of transmitting documents by fax; 4.4. Scalar quantizer with entropy constraint; 4.4.1. Introduction; 4.4.2. Lloyd-Max quantizer; 4.4.3. Quantizer with entropy constraint; 4.4.3.1. Expression for the entropy; 4.4.3.2. Jensen inequality; 4.4.3.3. Optimum quantizer; 4.4.3.4. Gaussian source; 4.5. Capacity of a discrete memoryless channel; 4.5.1. Introduction
- 4.5.2. Mutual information
