LEADER 05811nam 2200841 a 450 001 9910819986703321 005 20200520144314.0 010 $a9781118616611 010 $a1118616618 010 $a9781118616628 010 $a1118616626 010 $a9781299315228 010 $a1299315224 010 $a9781118616550 010 $a1118616553 035 $a(CKB)2560000000100605 035 $a(EBL)1144002 035 $a(SSID)ssj0000834297 035 $a(PQKBManifestationID)11460256 035 $a(PQKBTitleCode)TC0000834297 035 $a(PQKBWorkID)10980614 035 $a(PQKB)10541248 035 $a(Au-PeEL)EBL1144002 035 $a(CaPaEBR)ebr10674779 035 $a(CaONFJC)MIL462772 035 $a(CaSebORM)9781118616628 035 $a(MiAaPQ)EBC1144002 035 $a(OCoLC)830160910 035 $a(PPN)171170172 035 $a(OCoLC)875001751 035 $a(OCoLC)ocn875001751 035 $a(OCoLC)842932698 035 $a(FINmELB)ELB178692 035 $a(Perlego)1012769 035 $a(EXLCZ)992560000000100605 100 $a20130419d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aTools for signal compression /$fNicolas Moreau 205 $a1st edition 210 $aLondon $cISTE ;$aHoboken, N.J. $cJohn Wiley & Sons$dc2011 215 $a1 online resource (216 p.) 225 1 $aISTE 300 $aDescription based upon print version of record. 311 08$a9781848212558 311 08$a1848212550 320 $aIncludes bibliographical references and index. 327 $aCover; 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 327 $a1.3.2.2. 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 327 $a2.5.5. 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 327 $a4.2. 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 327 $a4.3. 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 327 $a4.5.2. Mutual information 330 $aThis book presents tools and algorithms required to compress/uncompress signals such as speech and music. These algorithms are largely used in mobile phones, DVD players, HDTV sets, etc. In a first rather theoretical part, this book presents the standard tools used in compression systems: scalar and vector quantization, predictive quantization, transform quantization, entropy coding. In particular we show the consistency between these different tools. The second part explains how these tools are used in the latest speech and audio coders. The third part gives Matlab programs simulating t 410 0$aISTE publications. 606 $aSound$xRecording and reproducing 606 $aData compression (Telecommunication) 606 $aSpeech processing systems 615 0$aSound$xRecording and reproducing. 615 0$aData compression (Telecommunication) 615 0$aSpeech processing systems. 676 $a621.389/3 700 $aMoreau$b Nicolas$f1945-$01693430 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910819986703321 996 $aTools for signal compression$94071219 997 $aUNINA