Concurrent Evolution of Pixel Predictor and Context Modeling for Image Coding

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Seishi Takamura and Atsushi Shimizu",
  title =        "Concurrent Evolution of Pixel Predictor and Context
                 Modeling for Image Coding",
  booktitle =    "2016 IEEE International Conference on image
                 Processing, ICIP",
  year =         "2016",
  editor =       "Fernando Pereira and Gaurav Sharma",
  pages =        "2147--2151",
  address =      "Phoenix, Arizona, USA",
  month =        "25-28 " # sep,
  organisation = "IEEE Signal Processing Society",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 genetic programming, image coding, pixel predictor,
                 context modelling expression: Poster",
  URL =          "",
  DOI =          "doi:10.1109/ICIP.2016.7532738",
  size =         "5 pages",
  abstract =     "Lossless image coding process predicts the value of
                 current pixel from previously decoded pixel values.
                 Then the prediction error is classified according to
                 the context model. This classification splits the
                 sources with different distributions and hence reduce
                 the total entropy of the prediction error signals. In
                 the literature, the predictor has been intensively
                 studied. Some evolutionary approaches have been applied
                 to generate a predictor to improve compression
                 performance. However, the context modelling method has
                 not relatively been well studied. We propose and
                 investigate a novel method to automatically obtain
                 evolved pair of pixel predictor and context modeling.
                 Simulation results show 1.32-3.90percent bit-rate
                 reduction against the pair of predictor and context
                 modeler of one of the best conventional methods
                 (CALIC). It is also demonstrated that the evolved
                 algorithm's size is more compact than former results.
                 We also found that context modeler is evolved in more
                 complex form than the predictor.",
  notes =        "ICIP 2016 Image and Video Coding. paper MPA-P3.3

                 NTT Corporation",

Genetic Programming entries for Seishi Takamura Atsushi Shimizu