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Image Compression Using Pixel Neural Networks

  • Wladyslaw Skarbek
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 42)

Abstract

A subclass of recurrent neural networks, called Pixel Neural Networks (PNN) is defined. N = mn neural (pixel) elements are located inmxn array. Each pixel has few input links, e.g. less than 10~5iV. The limit state (if exists) defines a 2D pattern, i.e. an image. Several sufficient conditions for the deterministic and stochastic convergence of PNN network are given. For LOPNN — a special subclass of PNN, necessary and sufficient condition for the convergence is found. It appears that PNN networks can represent real-life images and fractal compression operators can be implemented by networks from the special subclass FBPNN. Therefore they can be used for still image compression. Moreover, FBPNN representation enables reducing of the decoding time and the space use for about 50%. Finally, PNN networks can be used as components of a high performance associative memory.

Keywords

Face Recognition Discrete Cosine Transform Image Compression Associative Memory Recurrent Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Wladyslaw Skarbek
    • 1
  1. 1.Multimedia Laboratory Faculty of Electronics and Information Technology WarsawUniversity of TechnologyWarszawaPoland

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