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Proceedings Paper

Adaptive model for mixed binary image coding
Author(s): Takahiro Hongu; Takeshi Agui; Hiroshi Nagahashi
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Paper Abstract

In recent facsimile application fields, mixed documents comprising characters and photographs have come to be generally treated. Following this trend, the `joint bi-level image group (JBIG)' of ISO/IEC/JTC1/SCWG9 and CCITT/SG VIII prepared an international standard for the encoding of binary images obtained by quantizing mixed documents into binary levels. This JBIG coding scheme consists of two parts: (1) the modeling part based on binary Markov model referring to 10 pixels surrounding a current pixel to be encoded and (2) the coding part based on an adaptive arithmetic compression coder. This paper presents an adaptive model for mixed binary images, which realizes higher compression efficiency than the typical Markov model of the JBIG scheme. We describe two significant characteristics, generalized model and area classification. The generalized model refers not only to neighboring pixels like the typical Markov model, but also to a predicted gray-level calculated pixel by pixel from previously scanned pixels near a current pixel. The area classification classifies mixed binary images including both characters and halftone images into two types of areas. In this adaptive model, the typical Markov model and the generalized model for halftone images are selected according to the types of the areas. As a result, the adaptive model reduces the values of Markov entropy of mixed binary images and realizes high compression efficiency.

Paper Details

Date Published: 22 October 1993
PDF: 12 pages
Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); doi: 10.1117/12.157862
Show Author Affiliations
Takahiro Hongu, NEC Corp. (Japan)
Takeshi Agui, Tokyo Institute of Technology (Japan)
Hiroshi Nagahashi, Tokyo Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 2094:
Visual Communications and Image Processing '93
Barry G. Haskell; Hsueh-Ming Hang, Editor(s)

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