Single bit-map block truncation coding of color images using a Hopfield neural network

Shen Chuan Tai, Yih Chuan Lin, Jung Feng Lin

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper describes a new single bit-map block truncation coding (SBBTC) scheme that works with a Hopfield neural network (HNN) for the coding of color images. An incoming color block is encoded using three block-truncation coding (BTC) encoders with a common bit map for each of the three primary color planes. An HNN is used to generate a good single bit map in the SBBTC by clustering the color vectors in the block into two classes. The considering block is then encoded by the generated bit map and the two reconstruction colors each associated to one of the two classes. In comparison with other existing methods, the proposed color BTC gives the best mean-square error (MSE) performance at the same bit rate.

Original languageEnglish
Pages (from-to)211-228
Number of pages18
JournalInformation sciences
Volume103
Issue number1-4
DOIs
Publication statusPublished - 1997 Dec

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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