Books about Jpeg (found 121 titles)

Format de Fichier Graphique: Portable Network Graphics, Graphics Interchange Format, Scalable Vector Graphics, Jpeg 2000, Image Matricielle (French Edition)

Les achats comprennent une adh?sion ? l'essai gratuite au club de livres de l'?diteur, dans lequel vous pouvez choisir parmi plus d'un million d'ouvrages, sans frais. Le livre consiste d'articles Wikipedia sur : Portable Network Graphics, Graphics Interchange Format, Scalable Vector Graphics, Jpeg 2000, Image Matricielle, Windows Bitmap, Vector Markup Language, Animated Portable Network Graphics, Progressive Graphics File, Portable Pixmap, Touchgraph, Multiple-Image Network Graphics, Digital Negative, Tagged Image File Format, Windows Metafile, Jpeg Network Graphics, Pdf/x, Hd Photo, Ico, Pcx, Jpeg File Interchange Format, Truevision Targa, Openexr, Microsoft Document Imaging Format, Format de Fichier de Polygones, Digital Picture Exchange, Small Web Format, Encapsulated Postscript, Openjpeg, Ligne de Balayage, Ebox, Xcf, Cineon, Adobe Illustrator Artwork, Jpeg-Ls, Biovision Hierarchy, Openraster, Enhanced Compression Wavelet, Psd, Fxg. Non illustr?. Mises ? jour gratuites en ligne. Extrait : Diff?rence entre les images matricielles et vectorielles.Les images vectorielles peuvent ?tre agrandies ? l'infini. qui, traduit de l'anglais, signifie «?graphique vectoriel adaptable?», et couramment abr?g? par SVG, est un format de donn?es con?u pour d?crire des ensembles de graphiques vectoriels et bas? sur XML. Ce format inspir? directement du VML et du PGML est sp?cifi? par le . Le SVG est tr?s utilis? dans le monde de la cartographie et du t?l?phone portable. Les coordonn?es, dimensions et structures des objets vectoriels sont indiqu?es sous forme num?rique dans le document XML. Un syst?me sp?cifique de style (CSS ou XSL) permet d'indiquer les couleurs et les polices de caract?res ? utiliser. Ce format g?re quelques formes g?om?triques de base (rectangles, ellipses, etc.), mais aussi des chemins (), qui utilisent les courbes de B?zier et permettent ainsi d'obtenir presque n'importe quelle forme. Le remplissage peut se faire ? l'aide...http://booksllc.net/?l=fr

Compression D'image: Portable Network Graphics, Joint Photographic Experts Group, Transform?e En Cosinus Discr?te, Jpeg, Jpeg 2000 (French Edition)

Les achats comprennent une adh?sion ? l'essai gratuite au club de livres de l'?diteur, dans lequel vous pouvez choisir parmi plus d'un million d'ouvrages, sans frais. Le livre consiste d'articles Wikipedia sur : Portable Network Graphics, Joint Photographic Experts Group, Transform?e En Cosinus Discr?te, Jpeg, Jpeg 2000, Quantification, Compression par Ondelettes, Compression Fractale, Algorithme de Lloyd-Max, Structural Similarity, Peak Signal to Noise Ratio, Quantification Vectorielle, Jpeg-Ls, Jbig. Non illustr?. Mises ? jour gratuites en ligne. Extrait : La transform?e en cosinus discr?te ou TCD (de l'anglais : DCT ou Discrete Cosine Transform) est une transformation proche de la transform?e de Fourier discr?te (DFT). Le noyau de projection est un cosinus et cr?e donc des coefficients r?els, contrairement ? la DFT, dont le noyau est une exponentielle complexe et qui cr?e donc des coefficients complexes. On peut cependant exprimer la DCT en fonction de la DFT, qui est alors appliqu?e sur le signal sym?tris?. La variante la plus courante de la transform?e en cosinus discret est la DCT type-, souvent simplement appel?e « la DCT ». Son inverse, qui correspond au type- est souvent simplement appel?e « IDCT ». Une DCT 2D compar?e ? une DFT. Les zones claires repr?sentent les coefficients non-nuls La DCT est une fonction lin?aire inversible R ? R ou de mani?re ?quivalente une matrice carr?e N ? N inversible. Il existe plusieurs l?g?res variantes de la DCT. Voici les quatre types les plus connus. Le d?veloppement des algorithmes de calcul rapide des transform?es DCT se basent sur la possibilit? de d?composer la matrice de d?finition sous forme d'un produit de matrices dont le calcul est plus simple, et permet de r?duire le nombre de multiplications scalaires, en profitant des identit?s remarquables de p?riodicit? et sym?tries des fonctions sinuso?dales. Ainsi, on peut d?composer toute transform?e DCT de R en transform?es plus simpl...http://booksllc.net/?l=fr

Compression Vid?o: Transcodage, Webm, Moving Picture Experts Group, Mpeg-4, Codage Vid?o Scalable, Mjpeg2000, Mpeg-1, Mpeg-2, Motion Jpeg (French Edition)

Les achats comprennent une adh?sion ? l'essai gratuite au club de livres de l'?diteur, dans lequel vous pouvez choisir parmi plus d'un million d'ouvrages, sans frais. Le livre consiste d'articles Wikipedia sur : Transcodage, Webm, Moving Picture Experts Group, Mpeg-4, Codage Vid?o Scalable, Mjpeg2000, Mpeg-1, Mpeg-2, Motion Jpeg, Mpeg-4 Part 2. Non illustr?. Mises ? jour gratuites en ligne. Extrait : La compression vid?o est une m?thode de compression de donn?es, qui consiste ? r?duire la quantit? de donn?es, en limitant au maximum l'impact sur la qualit? visuelle de la vid?o. L'int?r?t de la compression vid?o est de r?duire les co?ts de stockage et de transmission des fichiers vid?o. Les s?quences vid?o contiennent une tr?s grande redondance statistique, aussi bien dans le domaine temporel que dans le domaine spatial. La propri?t? statistique fondamentale sur laquelle les techniques de compression se fondent, est la corr?lation entre pixels. Cette corr?lation est ? la fois spatiale, les pixels adjacents de l'image courante sont similaires, et temporelle, les pixels des images pass?es et futures sont aussi tr?s proches du pixel courant. Ainsi, on suppose que l'importance d'un pixel particulier de l'image peut ?tre pr?vue ? partir des pixels voisins de la m?me image (utilisant des techniques de codage intra-image) ou des pixels d'une image voisine (utilisant des techniques inter-image). Intuitivement il est clair que dans certaines circonstances, par exemple, lors d'un changement de plan d'une s?quence vid?o, la corr?lation temporelle entre pixels entre images voisines est petite, voir nulle. Dans ce cas, ce sont les techniques de codage dites Intra qui sont les mieux appropri?es, afin d'exploiter la corr?lation spatiale pour r?aliser une compression efficace de donn?es. Les algorithmes de compression vid?o de type MPEG utilisent une transformation appel?e DCT (pour Discrete Cosine Transform), sur des blocs de 8x8 pixels, pour ...http://booksllc.net/?l=fr

Datenkompression: Entropiekodierung, Jpeg, Wavelet-Kompression, Arithmetisches Kodieren, Transformationskodierung, Zip, Codec (German Edition)

Kapitel: Entropiekodierung, Jpeg, Wavelet-Kompression, Arithmetisches Kodieren, Transformationskodierung, Zip, Codec, Lempel-Ziv-Welch-Algorithmus, 7z, Shannon-Fano-Kodierung, Burrows-Wheeler-Transformation, Advanced Video Codec High Definition, Jpeg 2000, Sequitur, Bildkompression, Java Archive, Kompression Ausf?hrbarer Programmdateien, Rar, Lz78, Bereichskodierung, S3 Texture Compression, Deduplizierung, Lempel-Ziv-Markow-Algorithmus, Laufl?ngenkodierung, Lz77, Move to Front, Lha, Djvu, Pr?diktive Kodierung, H.323, Fraktale Bildkompression, Delta-Kodierung, Rate-Distortion-Theorie, Prediction by Partial Matching, H.450, Context Adaptive Binary Arithmetic Coding, Chm, Joint Photographic Experts Group, Xz, Gibbssches Ph?nomen, Reduced Offset Lempel Ziv, Ace, Web Open Font Format, Progressive Kompression, Varicode, Jbig, Canterbury Corpus, Context Adaptive Variable Length Coding, Enhanced Compressed Wavelet, Texturkomprimierung, Vektorquantisierung, Phar, Downsampling, W?rterbuchkompression, H.-Standards, Ultra Low Delay Coder, Tokenbasierte Kompression, Lzx-Algorithmus, Lempel-Ziv-Storer-Szymanski-Algorithmus, Lossless Transform Audio Compression, 3dc, Jb2, Lempel-Ziv-Stac-Algorithmus. Aus Wikipedia. Nicht dargestellt. Auszug: Other reasons this message may be displayed: ...http://booksllc.net/?l=de

JPEG XR

High Quality Content by WIKIPEDIA articles! JPEG XR is a still-image compression standard and file format for continuous tone photographic images, based on technology originally developed and patented by Microsoft as a part of the Windows Media family. It supports both lossy and lossless compression, and is the preferred image format for Ecma-388 Open XML Paper Specification documents. Support for file format is available as part of Windows Imaging Component, .NET Framework, and Windows Vista/Windows 7.

An Efficient Implementation of High-Speed JPEG2000 Encoder/Decoder: ALGORITHMS, SPECIFICATIONS, DESIGN SPACE EXPLORATION

This book presents a complete and up-to-date description of an efficient FPGA implementation of high-speed JPEG2000 using formal design methodologies namely SpecC and A3, in a systematic and clear manner. JPEG2000 proposes a large set of advanced features for today's internet and multimedia applications. At the heart of JPEG2000 are two complex DSP algorithms: Discrete Wavelet Transform (DWT) and Embedded Block Coding with Optmized Truncation (EBCOT). Both algorithms are complex, compute intensive and very difficult to optimize in software implementation for real-time imaging and multimedia applications. A hardware implementation brings a solution to this complexity for real-time applications. This book proposes flexible and efficient architectures for DWT, matching arithmetic coder (MQ coder) and EBCOT units of the JPEG2000 system. The implemented parallel structure increases throughput off the Tier-1 (EBCOT and MQC) and DWT by factors of 301 and 164 cycles respectively. Execution results from software only and FPGA implemented JPEG2000 is compared and analyzed.

Compression Algorithms: Lossless Compression Algorithms, Lossy Compression Algorithms, Huffman Coding, Lossless Data Compression, Jpeg

Purchase includes free access to book updates online and a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: Lossless Compression Algorithms, Lossy Compression Algorithms, Huffman Coding, Lossless Data Compression, Jpeg, Arithmetic Coding, Run-Length Encoding, Lempel-ziv-welch, Entropy Encoding, Vorbis, Deflate, Burrows-wheeler Transform, Fractal Compression, Shannon-fano Coding, Wavelet Compression, Linear Predictive Coding, Lz77 and Lz78, Vector Quantization, Packbits, Bzip2, Fibonacci Coding, Elias Gamma Coding, Prefix Code, Transform Coding, Vocoder, Elias Delta Coding, Range Encoding, Companding, Golomb Coding, Delta Encoding, Advanced Audio Coding, Mpeg-4 Part 3, Jpeg Xr, Paq, Adx, Lossless Jpeg, Lempel-Ziv-Markov Chain Algorithm, S3 Texture Compression, Dynamic Markov Compression, Universal Code, Jbig2, Variable-Length Code, Package-Merge Algorithm, Block Truncation Coding, Truncated Binary Encoding, Move-To-Front Transform, Prediction by Partial Matching, Embedded Zerotrees of Wavelet Transforms, Mrsid, Adam7 Algorithm, Adaptive Huffman Coding, Aac-Ld, Felics, Elias Omega Coding, Canonical Huffman Code, 3dc, Lzx, Sheervideo, Line Spectral Pairs, Lzwl, Huffyuv, Negafibonacci Coding, Lempel-Ziv-Storer-Szymanski, Freearc, Unary Coding, Lempel-Ziv-Oberhumer, Context-Adaptive Binary Arithmetic Coding, Shannon-Fano-Elias Coding, Incremental Encoding, Parametric Stereo, Statistical Lempel Ziv, Http Compression, Lagarith, Exponential-Golomb Coding, Incompressible String, Sequitur Algorithm, Chain Code, Levenshtein Coding, Byte Pair Encoding, Recursive Indexing, Context-Adaptive Variable-Length Coding, Context Tree Weighting, Microsoft Video 1, Msu Lossless Video Codec, Wavelet Scalar Quantization, Lzrw, Ccsds 122.0-B-1, Log Area Ratios, Reduced Offset Lempel Ziv, Algorithm Bstw, Lzjb, Quad, Modified Huffman Coding. Excerpt: In computing, JPEG (prono... More: http://booksllc.net/?id=16009

Detecting Information Hidden in JPEG Images: An investigation of machine learning methods for image steganalysis

This book describes using artificial neural networks as a potential approach for the steganalysis of JPEG images. The features that would be used by the detection algorithm have been previously suggested in various literature. These have given the best published results. The features are discussed in detail in the book. However, most earlier approaches have used SVMs for the detection process, while the proposed approach is based on using neural networks instead. Several experiments were conducted while researching for this book to see if the number of features required for adequate detection accuracy can be reduced without impacting the accuracy. The results from reduced feature sets are benchmarked according to the detection accuracy that was achieved as well as the time taken for the extraction of those particular features. A point to note is that the discusses the problem of detecting messages hidden in JPEG images, rather than actually recovering the hidden messages.

Wavelets: Jpeg 2000, Wavelet Compression, Wavelet, Discrete Wavelet Transform, Multigrid Method, Legendre Wavelet, Icer, Mathieu Wavelet

Purchase includes free access to book updates online and a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: Jpeg 2000, Wavelet Compression, Wavelet, Discrete Wavelet Transform, Multigrid Method, Legendre Wavelet, Icer, Mathieu Wavelet, Prolate Spheroidal Wave Functions, Polyphase Matrix, Refinable Function, Complex Wavelet Transform, Fast Wavelet Transform, Generalized Lifting, Transfer Matrix, Lifting Scheme, Harmonic Wavelet Transform, Embedded Zerotrees of Wavelet Transforms, Multiresolution Analysis, Ali Akansu, Coiflet, Filter Bank, Curvelet, Wavelet Series, Dual Wavelet, Cascade Algorithm, Second Generation Wavelet Transform, Stationary Wavelet Transform, Gabor Atom, Wavelet Packet Decomposition, Bandelet, Pixlet, List of Wavelet-Related Transforms, Contourlet, Set Partitioning in Hierarchical Trees, Wavelet Modulation, Modified Morlet Wavelet, Scaleogram, Dynamic Link Matching. Excerpt: Ali N. Akansu is a Turkish American scientist best known for his contributions to the theory and applications of sub-band and wavelet transforms .Biography Akansu received his B.S. degree from the Istanbul Technical University , Turkey, in 1980, his M.S. and Ph.D degrees from the Polytechnic University , Brooklyn, New York, in 1983 and 1987, respectively, all in Electrical Engineering. Since 1987, he has been with the New Jersey Institute of Technology where he is a Professor of Electrical

Lossy Compression Algorithms: Jpeg, Lossy Compression, Vorbis, Fractal Compression, Wavelet Compression, Linear Predictive Coding

Purchase includes free access to book updates online and a free trial membership in the publisher's book club where you can select from more than a million books without charge. Chapters: Jpeg, Lossy Compression, Vorbis, Fractal Compression, Wavelet Compression, Linear Predictive Coding, Vector Quantization, Transform Coding, Vocoder, Companding, Advanced Audio Coding, Mpeg-4 Part 3, Jpeg Xr, Adx, S3 Texture Compression, Jbig2, Block Truncation Coding, Mrsid, Aac-Ld, Quantization, Felics, 3dc, Line Spectral Pairs, Jbig, Parametric Stereo, Microsoft Video 1, Wavelet Scalar Quantization, Log Area Ratios. Excerpt: 3Dc (FourCC ATI2), also known as DXN, is a lossy data compression algorithm for normal maps invented and first implemented by ATI . It builds upon the earlier DXT5 algorithm and is an open standard . 3Dc is now implemented by both ATI and NVIDIA .Target Application The target application, normal mapping, is an extension of bump mapping that simulates lighting on geometric surfaces by reading surface normals from a rectilinear grid analogous to a texture map - giving simple models the impression of increased complexity. This additional channel however increases the load on the graphics system's memory bandwidth. Pre-existing lossy compression algorithms implemented on consumer 3d hardware lacked the precision necessary for reproducing normal maps without excessive visible artefacts, justifying the development of 3Dc.Although 3Dc was formally introduced with the ATI x800 series cards, there is also an S3TC compatible version planned for the older R3xx series, and cards from other companies. The quality and compression will not be as good, but the visual errors will still be significantly less than offered by standard S3TC.Algorithm Surface normals are three dimensional vectors of unit length. Because of the length constraint only two elements of any normal need to be stored. The input is therefore an array of two dimensional values.Compression is performed ...

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