Morphological dilation image coding with context weights prediction |
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Authors: | Jiaji Wu Anand Paul Yan Xing Yong Fang Jechang Jeong Licheng Jiao Guangming Shi |
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Affiliation: | a Key laboratory of Intelligent Perception and Image Understanding—Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Shaanxi 710071, China;b Department of Electronics Engineering, Hanyang University, Seoul 133-791, South Korea;c College of Information Engineering, Northwest A&F University, Yangling 712100, China |
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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. |
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Keywords: | Quad-tree coding Morphological dilation Variable-length group test coding Weights training |
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