LEADER 01055nam a22002651i 4500 001 991002142409707536 005 20040205182346.0 008 040407s1985 it a||||||||||||||||ita 020 $a8842200328 035 $ab12877098-39ule_inst 035 $aARCHE-085671$9ExL 040 $aDip.to Scienze Storiche$bita$cA.t.i. Arché s.c.r.l. 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Pandora Sicilia s.r.l. 082 04$a658.40120945 100 1 $aFoglio, Antonio$077562 245 10$aAlleanze strategiche per l'impresa :$bscegliere e gestire le alleanze vincenti /$cAntonio Foglio 260 $aMilano :$bAngeli,$c[1996] 300 $a281 p. ;$c23 cm 490 $aAzienda moderna ;$v313 650 4$aConsorzi$xGestione 650 4$aJoint ventures$xGestione 907 $a.b13214858$b11-03-19$c23-09-04 912 $a991000398159707536 945 $aLE025 ECO 658.4 FOG01.01$g1$i2025000173682$lle025$nCatalogato 2019$o-$pE0.00$q-$rl$s- $t0$u7$v2$w7$x0$y.i13870403$z23-09-04 996 $aAlleanze strategiche per l'impresa$9507714 997 $aUNISALENTO 998 $ale025$b23-09-04$cm$da $e-$fita$git $h0$i1 LEADER 05646nam 2200745Ia 450 001 9910818500803321 005 20200520144314.0 010 $a9786610272754 010 $a9781280272752 010 $a1280272759 010 $a9780470322154 010 $a0470322152 010 $a9780471653752 010 $a0471653756 010 $a9780471653745 010 $a0471653748 035 $a(CKB)1000000000019104 035 $a(EBL)221321 035 $a(OCoLC)57585202 035 $a(SSID)ssj0000186351 035 $a(PQKBManifestationID)11197184 035 $a(PQKBTitleCode)TC0000186351 035 $a(PQKBWorkID)10216739 035 $a(PQKB)11227013 035 $a(MiAaPQ)EBC221321 035 $a(Au-PeEL)EBL221321 035 $a(CaPaEBR)ebr10114023 035 $a(CaONFJC)MIL27275 035 $a(Perlego)2770252 035 $a(EXLCZ)991000000000019104 100 $a20040512d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aJPEG2000 standard for image compression $econcepts, algorithms and VLSI architectures /$fTinku Acharya, Ping-Sing Tsai 205 $a1st ed. 210 $aHoboken, N.J. ;$a[Chichester] $cWiley-Interscience$dc2005 215 $a1 online resource (294 p.) 300 $aDescription based upon print version of record. 311 08$a9780471484226 311 08$a0471484229 320 $aIncludes bibliographical references and index. 327 $aJPEG2000 Standard for Image Compression; Contents; Preface; 1 Introduction to Data Compression; 1.1 Introduction; 1.2 Why Compression?; 1.2.1 Advantages of Data Compression; 1.2.2 Disadvantages of Data Compression; 1.3 Information Theory Concepts; 1.3.1 Discrete Memoryless Model and Entropy; 1.3.2 Noiseless Source Coding Theorem; 1.3.3 Unique Decipherability; 1.4 Classification of Compression algorithms; 1.5 A Data Compression Model; 1.6 Compression Performance; 1.6.1 Compression Ratio and Bits per Sample; 1.6.2 Quality Metrics; 1.6.3 Coding Delay; 1.6.4 Coding Complexity 327 $a1.7 Overview of Image Compression1.8 Multimedia Data Compression Standards; 1.8.1 Still Image Coding Standard; 1.8.2 Video Coding Standards; 1.8.3 Audio Coding Standard; 1.8.4 Text Compression; 1.9 Summary; References; 2 Source Coding Algorithms; 2.1 Run-length Coding; 2.2 Huffman Coding; 2.2.1 Limitations of Huffman Coding; 2.2.2 Modified Huffman Coding; 2.3 Arithmetic Coding; 2.3.1 Encoding Algorithm; 2.3.2 Decoding Algorithm; 2.4 Binary Arithmetic Coding; 2.4.1 Implementation with Integer Mathematics; 2.4.2 The QM-Coder; 2.5 Ziv-Lempel Coding; 2.5.1 The LZ77 Algorithm 327 $a2.5.2 The LZ78 Algorithm2.5.3 The LZW Algorithm; 2.6 Summary; References; 3 JPEG: Still Image Compression Standard; 3.1 Introduction; 3.2 The JPEG Lossless Coding Algorithm; 3.3 Baseline JPEG Compression; 3.3.1 Color Space Conversion; 3.3.2 Source Image Data Arrangement; 3.3.3 The Baseline Compression Algorithm; 3.3.4 Discrete Cosine Transform; 3.3.5 Coding the DCT Coefficients; 3.3.6 Decompression Process in Baseline JPEG; 3.4 Progressive DCT-based Mode; 3.5 Hierarchical Mode; 3.6 Summary; References; 4 Introduction to Discrete Wavelet Transform; 4.1 Introduction; 4.2 Wavelet Transforms 327 $a4.2.1 Discrete Wavelet Transforms4.2.2 Concept of Multiresolution Analysis; 4.2.3 Implementation by Filters and the Pyramid Algorithm; 4.3 Extension to Two-Dimensional Signals; 4.4 Lifting Implementation of the Discrete Wavelet Transform; 4.4.1 Finite Impulse Response Filter and Z-transform; 4.4.2 Euclidean Algorithm for Laurent Polynomials; 4.4.3 Perfect Reconstruction and Polyphase Representation of Filters; 4.4.4 Lifting; 4.4.5 Data Dependency Diagram for Lifting Computation; 4.5 Why Do We Care About Lifting?; 4.6 Summary; References; 5 VLSI Architectures for Discrete Wavelet Transforms 327 $a5.1 Introduction5.2 A VLSI Architecture for the Convolution Approach; 5.2.1 Mapping the DWT in a Semi-Systolic Architecture; 5.2.2 Mapping the Inverse DWT in a Semi-Systolic Architecture; 5.2.3 Unified Architecture for DWT and Inverse DWT; 5.3 VLSI Architectures for Lifting-based DWT; 5.3.1 Mapping the Data Dependency Diagram in Pipeline Architectures; 5.3.2 Enhanced Pipeline Architecture by Folding; 5.3.3 Flipping Architecture; 5.3.4 A Register Allocation Scheme for Lifting; 5.3.5 A Recursive Architecture for Lifting; 5.3.6 A DSP-Type Architecture for Lifting 327 $a5.3.7 A Generalized and Highly Programmable Architecture for Lifting 330 $aJPEG2000 Standard for Image Compression presents readers with the basic background to this multimedia compression technique and prepares the reader for a detailed understanding of the JPEG2000 standard, using both the underlying theory and the principles behind the algorithms of the JPEG2000 standard for scalable image compression. It introduces the VLSI architectures and algorithms for implementation of the JPEG2000 standard in hardware (not available in the current literature), an important technology for a number of image processing applications and devices such as digital camera, color fax 606 $aJPEG (Image coding standard) 606 $aImage compression 615 0$aJPEG (Image coding standard) 615 0$aImage compression. 676 $a621.367 700 $aAcharya$b Tinku$0725531 701 $aTsai$b Ping-Sing$f1962-$01653181 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910818500803321 996 $aJPEG2000 standard for image compression$94004331 997 $aUNINA