Accelerating pixel predictor evolution using edge-based class separation

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Takamura:2010:PCS,
  author =       "Seishi Takamura and Masaaki Matsumura and 
                 Hirohisa Jozawa",
  title =        "Accelerating pixel predictor evolution using
                 edge-based class separation",
  booktitle =    "Picture Coding Symposium (PCS 2010)",
  year =         "2010",
  month =        "8-10 " # dec,
  pages =        "106--109",
  abstract =     "Evolutionary methods based on genetic programming (GP)
                 enable dynamic algorithm generation, and have been
                 successfully applied to many areas such as plant
                 control, robot control, and stock market prediction.
                 However, one of the challenges of this approach is its
                 high computational complexity. Conventional image/video
                 coding methods such as JPEG and H.264 all use fixed
                 (non-dynamic) algorithms without exception. However,
                 one of the challenges of this approach is its high
                 computational complexity. In this article, we introduce
                 a GP-based image predictor that is specifically evolved
                 for each input image, as well as local image properties
                 such as edge direction. Via the simulation, proposed
                 method demonstrated ~180 times faster evolution speed
                 and 0.02-0.1 bit/pel lower bit rate than previous
                 method.",
  keywords =     "genetic algorithms, genetic programming, computational
                 complexity, dynamic algorithm generation, edge-based
                 class separation, evolutionary method, image coding,
                 image predictor, pixel predictor evolution, video
                 coding, image coding",
  DOI =          "doi:10.1109/PCS.2010.5702434",
  notes =        "NTT Cyber Space Laboratories, NTT Corporation. Also
                 known as \cite{5702434}",
}

Genetic Programming entries for Seishi Takamura Masaaki Matsumura Hirohisa Jozawa

Citations