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A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images.

Borut ŽalikDamjan StrnadDavid PodgorelecIvana KolingerováLuka LukačNiko LukačSimon KolmaničKrista Rizman ŽalikŠtefan Kohek
Published in: Entropy (Basel, Switzerland) (2023)
This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows-Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.
Keyphrases
  • deep learning
  • convolutional neural network
  • optical coherence tomography
  • health information
  • machine learning
  • social media