Morphological dilation image coding with context weights prediction
Morphological dilation image coding with context weights prediction
复制标题
具有上下文权重预测的形态扩张图像编码
DOI：
10.1016/j.image.2010.10.003
复制
发表时间：
2010-11-01
影响因子：
3.5
通讯作者：
Shi, Guangming
中科院分区：
文献类型：
--
作者：
Wu, Jiaji;Paul, Anand;Shi, Guangming
提出了一种基于上下文权重预测的自适应形态膨胀图像编码方法。新的膨胀方法不是使用固定的模型，而是根据系数的预测显著性程度来决定系数是否需要膨胀。它包括两个关键的膨胀技术：（1）使用上下文权重控制膨胀过程，以减少无关系数的输出;（2）使用上下文权重的变长组测试编码，以调整编码顺序和尽可能少的比特数来呈现大概率事件。此外，我们还提出了一种新的上下文权重策略，可以更准确地预测系数的重要程度，可以用于两种膨胀技术。实验结果表明，我们提出的方法优于国家的艺术图像编码算法今天可用。(C)2010 Elsevier B. V.保留所有权利。
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. (C) 2010 Elsevier B.V. All rights reserved.