New results and measurements related to some tasks in object-oriented dynamic image coding using CNN universal chips

Tibor Kozek, Chai Wah Wu, A. Zarándy, Hua Chen, T. Roska, Murat Kunt, Leon O. Chua

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Cellular neural/nonlinear networks (CNN) are considered here for efficient implementation of the most computationally intensive steps of dynamic image coding. Several analogic CNN algorithms are presented for the generation of binary image masks and image decomposition. Measurement results for the first CNN universal chips executing an analogic algorithm for a reconstruction operator are also presented. Based on measured execution times, the viability of the CNN implementation of efficient but computationally expensive compression algorithms such as dynamic image coding is assessed.

Original languageEnglish
Pages (from-to)606-614
Number of pages9
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume7
Issue number4
DOIs
Publication statusPublished - 1997

Fingerprint

Nonlinear networks
Image coding
Binary images
Mathematical operators
Masks
Decomposition

Keywords

  • Computing architecture
  • Image coding
  • Image segmentation
  • Morphology
  • Multimedia
  • Neural networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

New results and measurements related to some tasks in object-oriented dynamic image coding using CNN universal chips. / Kozek, Tibor; Wu, Chai Wah; Zarándy, A.; Chen, Hua; Roska, T.; Kunt, Murat; Chua, Leon O.

In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 7, No. 4, 1997, p. 606-614.

Research output: Contribution to journalArticle

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