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Neural Video Compression Based on PVQ Algorithm

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10245))

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Abstract

In this paper we present a video compression algorithm based on predictive vector quantization, which is a combination of vector quantization and differential pulse code modulation. We optimized the algorithm using chroma subsampling which reduces the amount of information that needs to be processed. This allowed us to combine two color channels into one and thereby reduce the number of predictors and codebooks. Furthermore, we introduced inter-frames which only store regions that changed compared to previous frames, further decreasing the size of compressed data.

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Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Michał Knop & Tomasz Kapuściński

  2. Institute of Information Technology, Radom Academy of Economics, Domagalskiego Street 7a, 26-600, Radom, Poland

    Michał Knop & Tomasz Kapuściński

  3. Department of Computer Science, Georgia State University, P.O. Box 5060, Atlanta, GA, 30302-5060, USA

    Rafał Angryk

Authors
  1. Michał Knop
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  2. Tomasz Kapuściński
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  3. Rafał Angryk
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Corresponding author

Correspondence to Michał Knop .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Knop, M., Kapuściński, T., Angryk, R. (2017). Neural Video Compression Based on PVQ Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_48

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  • DOI: https://doi.org/10.1007/978-3-319-59063-9_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59062-2

  • Online ISBN: 978-3-319-59063-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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