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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Hierarchical Lossless Image Coding Using Cellular Neural Network
verfasst von : Seiya Takenouchi, Hisashi Aomori, Tsuyoshi Otake, Mamoru Tanaka, Ichiro Matsuda, Susumu Itoh
Erschienen in: Neural Information Processing. Theory and Algorithms
Verlag: Springer Berlin Heidelberg
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In this paper, a novel hierarchical lossless image coding scheme using the cellular neural network (CNN) is proposed. The coding architecture of the proposed method is based on the lifting scheme that is one of the scalable coding framework for still images, and its coding performance strongly depends on the prediction ability. To cope with this spontaneously characteristic, an image interpolation is modeled by an optimal problem that minimizes the prediction error. To achieve the high accuracy prediction with a low coding rate, two types of templates are used for dealing with the local structure of the image, and the CNN parameters are decided by the minimum coding rate learning. In the coding layer, the arithmetic coder with context modeling is used for obtaining a high coding efficiency. Experimental results in various standard test images suggest that the coding performance of our proposed method is better than that of conventional hierarchical coding schemes.