Morphological dilation image coding with context weights prediction

  • Authors:

  • Jiaji Wu;Anand Paul;Yan Xing;Yong Fang;Jechang Jeong;Licheng Jiao;Guangming Shi

  • Affiliations:

  • Key laboratory of Intelligent Perception and Image Understanding-Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Shaanxi 710071, China and Depar ...;Department of Electronics Engineering, Hanyang University, Seoul 133-791, South Korea;Key laboratory of Intelligent Perception and Image Understanding-Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Shaanxi 710071, China;College of Information Engineering, Northwest A&F University, Yangling 712100, China;Department of Electronics Engineering, Hanyang University, Seoul 133-791, South Korea;Key laboratory of Intelligent Perception and Image Understanding-Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Shaanxi 710071, China;Key laboratory of Intelligent Perception and Image Understanding-Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Shaanxi 710071, China

  • Venue:
  • Image Communication
  • Year:
  • 2010

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Abstract

This paper proposes an adaptive morphological dilation image coding with context weights prediction. The new dilation method is not to use fixed models, but to decide whether a coefficient needs to be dilated or not according to the coefficient's predicted significance degree. It includes two key dilation technologies: (1) controlling dilation process with context weights to reduce the output of insignificant coefficients and (2) using variable-length group test coding with context weights to adjust the coding order and cost as few bits as possible to present the events with large probability. Moreover, we also propose a novel context weight strategy to predict a coefficient's significance degree more accurately, which can be used for two dilation technologies. Experimental results show that our proposed method outperforms the state of the art image coding algorithms available today.