Proceedings of the Korean Institute of Intelligent Systems Conference (한국지능시스템학회:학술대회논문집)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Motion Compression Method by Min S-norm Composite Fuzzy Relational Equations

  • Nobuhara, Hajime (Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology) ;
  • Hirota, Kaoru (Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology)
  • Published : 2003.09.01
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Abstract

A motion compression method by min s-norm composite fuzzy relational equations (dual-MCF) is proposed, where a motion sequence is divided into intra-pictures (I-pictures) and predictive-pictures (P-pictures). The I-pictures and the P-pictures are compressed by using uniform coders and non-uniform coders, respectively. A design method of non-uniform coders is proposed to perform an efficient compression and reconstruction of the P-pictures, based on the dual overlap level of fuzzy sets and a fuzzy equalization. An experiment using 10 P-pictures confirms that the root means square errors of the proposed method is decreased to 82.9％ of that of the uniform coders, under the condition that the compression rate is 0.0055. An experiment of motion compression and reconstruction is also presented to confirm the effectiveness of the dual-MCF based on the non-uniform coders.

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