05811nam 2200841 a 450 991081998670332120200520144314.097811186166111118616618978111861662811186166269781299315228129931522497811186165501118616553(CKB)2560000000100605(EBL)1144002(SSID)ssj0000834297(PQKBManifestationID)11460256(PQKBTitleCode)TC0000834297(PQKBWorkID)10980614(PQKB)10541248(Au-PeEL)EBL1144002(CaPaEBR)ebr10674779(CaONFJC)MIL462772(CaSebORM)9781118616628(MiAaPQ)EBC1144002(OCoLC)830160910(PPN)171170172(OCoLC)875001751(OCoLC)ocn875001751(OCoLC)842932698(FINmELB)ELB178692(Perlego)1012769(EXLCZ)99256000000010060520130419d2011 uy 0engur|n|---|||||txtccrTools for signal compression /Nicolas Moreau1st editionLondon ISTE ;Hoboken, N.J. John Wiley & Sonsc20111 online resource (216 p.)ISTEDescription based upon print version of record.9781848212558 1848212550 Includes bibliographical references and index.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 minimization1.3.2.2. 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 quantization2.5.5. 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. Introduction4.2. 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 coding4.3. 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. Introduction4.5.2. Mutual informationThis 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 and vector quantization, predictive quantization, transform quantization, entropy coding. In particular we show the consistency between these different tools. The second part explains how these tools are used in the latest speech and audio coders. The third part gives Matlab programs simulating tISTE publications.SoundRecording and reproducingData compression (Telecommunication)Speech processing systemsSoundRecording and reproducing.Data compression (Telecommunication)Speech processing systems.621.389/3Moreau Nicolas1945-1693430MiAaPQMiAaPQMiAaPQBOOK9910819986703321Tools for signal compression4071219UNINA