Evolved nonlinear predictor functions for lossless image compression

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@InProceedings{Barresi:2014:GECCOcomp,
  author =       "Kevin M. Barresi",
  title =        "Evolved nonlinear predictor functions for lossless
                 image compression",
  booktitle =    "GECCO Comp '14: Proceedings of the 2014 conference
                 companion on Genetic and evolutionary computation
                 companion",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming: Poster",
  pages =        "129--130",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2598503",
  DOI =          "doi:10.1145/2598394.2598503",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Due to the increased quantity of digital data,
                 especially in the form of digital images, the need for
                 effective image compression techniques is greater than
                 ever. The JPEG lossless mode relies on predictive
                 coding, in which accurate predictive models are
                 critical. This study presents an efficient method of
                 generating predictor models for input images via
                 genetic programming. It is shown to always produce
                 error images with entropy equal to or lower than those
                 produced by the JPEG lossless mode. This method is
                 demonstrated to have practical use as a real-time
                 asymmetric image compression algorithm due to its
                 ability to quickly and reliably derive prediction
                 models.",
  notes =        "Also known as \cite{2598503} Distributed at
                 GECCO-2014.",
}

Genetic Programming entries for Kevin M Barresi

Citations