{
  "annotation_id": "9845eb67-37e4-48c5-bb89-90c8baefa873",
  "date_created": "2026-03-02T18:00:43.214000Z",
  "date_modified": "2026-03-02T18:00:43.214000Z",
  "file_hash": "e4cc8489805f16440a37d30b6f2dc177cb026d65f92656656e23febc1a1cbcdc",
  "private": false,
  "record": {
    "abstract": "We report the development of a scalar quantization approach that helps build\ntables of decision and reconstruction levels for any probability density\nfunction (pdf). Several example pdf\u0027s are used for illustration:\n  Uniform, Gaussian, Laplace, one-sided Rayleigh, and Gamma (One sided and\ndouble-sided symmetrical). The main applications of the methodology are\nprincipally aimed at Multiresolution Image compression where generally the\nStretched Exponential pdf is encountered. Specialising to this important case,\nwe perform quantization and information entropy calculations from selected\nmedical MRI (Magnetic Resonance Imaging) pictures of the human brain. The image\nhistograms are fitted to a Stretched exponential model and the corresponding\nentropies are compared.",
    "arxiv_id": "physics/0211064",
    "authors": [
      "C. Tannous"
    ],
    "categories": [
      "physics.med-ph"
    ],
    "title": "Non-linear quantization for arbitrary distributions and applications to   Medical Image Processing",
    "url": "https://arxiv.org/abs/physics/0211064"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "05877292-ea1b-47df-8e75-1b66cb31915c",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}