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Reversible compression of medical images with adaptive context selection

机译:具有自适应上下文选择的医学图像可逆压缩

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Abstract: oved version of an efficient method for the reversible compression of digitized medical images is described. The modifications made in this version are aimed at reducing some of the limitations imposed by the original method. As in the original method, the improved method uses the well-known linear prediction technique to decorrelate a given image. A statistical source model with multiple contexts is employed to model the sequence of decorrelated image pixels. The selection of contexts for the source model is based on the horizontal and vertical components of the gradient in the given image as well as the predicted gray-level value of a pixel. The selection procedure is however entirely adaptive in the improved method, whereas it is only partially adaptive in the original method. The source model statistics are also calculated adaptively. The decorrelated image pixels are encoded using the appropriate contextual statistics with the arithmetic coding technique. Experiments on three groups of medical images show that the improved method achieves satisfactory compression performance.!12
机译:摘要:描述了一种可逆压缩数字化医学图像的有效方法的另一种形式。此版本中进行的修改旨在减少原始方法带来的某些限制。与原始方法一样,改进后的方法使用众所周知的线性预测技术对给定图像进行解相关。具有多个上下文的统计源模型用于对去相关图像像素的序列进行建模。源模型的上下文选择基于给定图像中渐变的水平和垂直分量以及像素的预测灰度值。然而,选择过程在改进方法中是完全自适应的,而在原始方法中它仅是部分自适应的。源模型统计信息也可以自适应计算。去相关图像像素通过算术编码技术使用适当的上下文统计进行编码。对三组医学图像进行的实验表明,改进的方法获得了令人满意的压缩性能。!12

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