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Autor(en): 
  • Joshi Madhuri A.
  • Raval Mehul S.
  • Dandawate Yogesh H.
  • Joshi Kalyani R.
  • Metkar Shilpa P.
  • Image and Video Compression: Fundamentals, Techniques, and Applications 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2019  
    Genre:  EDV / Informatik 
     
    advanced image compression techniques / COMPUTERS / Optical Data Processing / data storage optimisation / Digital Signal Processing / graduate level resource / Image processing / image quality metrics / MATLAB Programming
    ISBN:  9780367378165 
    EAN-Code: 
    9780367378165 
    Verlag:  Taylor and Francis 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 234 mm / B 156 mm / D  
    Gewicht:  453 gr 
    Seiten:  236 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:

    Image and video signals require large transmission bandwidth and storage, leading to high costs. The data must be compressed without a loss or with a small loss of quality. Thus, efficient image and video compression algorithms play a significant role in the storage and transmission of data.

    Image and Video Compression: Fundamentals, Techniques, and Applications explains the major techniques for image and video compression and demonstrates their practical implementation using MATLAB® programs. Designed for students, researchers, and practicing engineers, the book presents both basic principles and real practical applications.

    In an accessible way, the book covers basic schemes for image and video compression, including lossless techniques and wavelet- and vector quantization-based image compression and digital video compression. The MATLAB programs enable readers to gain hands-on experience with the techniques. The authors provide quality metrics used to evaluate the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most important part of the signal while it is being sensed.

      



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