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Noncausal predictive lattice model for image compression

机译:图像压缩的非因果预测格模型

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摘要

In this paper, we introduce a noncausal predictive lattice model (NCPLM) for image compression. Owing to its full plane support, the NCPLM is a more effective model in reducing redundancy for improved image compression as compared to causal predictive models. To avoid instability and non-realizable synthesis problems in association with a noncausal support, the binary pyramid decomposition is adopted. The applications of the NCPLM for implementing lossless and lossy image CODEC are presented. Simulation results indicate that the proposed NCPLM is effective as a predictive algorithm for image compression.
机译:在本文中,我们介绍了一种用于图像压缩的非因果预测晶格模型(NCPLM)。由于其全平面支持,与因果预测模型相比,NCPLM是一种更有效的模型,可减少冗余以提高图像压缩率。为了避免与非因果支持相关的不稳定和无法实现的合成问题,采用了二元金字塔分解法。介绍了NCPLM在实现无损和有损图像编解码器中的应用。仿真结果表明,所提出的NCLPM作为图像压缩的预测算法是有效的。

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