<title>Adaptive model for mixed binary image coding</title>

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In recent facsimile application fields, mixed documents comprising characters and photographs have come to begenerally treated. Following this trend, the "Joint Bi-level Image Group (JBIG)" of ISO/IEC/JTC1/SCWG9 andCCITT/SG VIII prepared an international standard for the encoding of binary images obtained by quantizingmixed documents into binary levels. This JBIG coding scheme consists of two parts: 1) the modeling part basedon binary Markov model referring to 10 pixels surrounding a current pixel to be encoded and 2) the coding partbased on an adaptive arithmetic compression coder. This paper presents an adaptive model for mixed binaryimages, which realizes higher compression efficiency than the typical Markov model of the JBIG scheme. Wedescribe two significant characteristics, that is, generalized model and area classification, of this adaptive modelas follows. First, we propose a generalized model for halftone images generated by several methods, such as anerror diffusion method and an ordered dither method. The generalized model refers not only to neighboring pixelslike the typical Markov model, but also to a predicted gray-level calculated pixel by pixel from previously scannedpixels near a current pixel. Secondly we propose the area classification. The area classification classifies mixedbinary images including both characters and halftone images into two types of areas. The proposed adaptivemodel refers to several kinds of data: the neighboring pixels, the predicted gray-level and the type of the area.In this adaptive model, the typical Markov model and the generalized model for halftone images are selected

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詳細情報 詳細情報について

  • CRID
    1870302167797668096
  • DOI
    10.1117/12.157862
  • ISSN
    0277786X
  • データソース種別
    • OpenAIRE

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