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Cross-point regions on multiple bit planes for lossless images compression

机译:多个位平面上的交叉点区域可实现无损图像压缩

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This study presents cross-point regions for lossless image compression on multiple bit planes (CRICM), an algorithm for losslessly encoding and decoding images, especially medical images, by optimising on the probability of bits on different bit planes of cross points that are neighbour points of grey levels 2n. Based on Gray coding, Gray codes of cross points are determined on an adjacent data set because images characteristically contain data that do not change much in a specific area, then this effect is generalised for real data without losing generality for their statistical properties. This is especially true for medical images that have many regions with the same grey levels. The Gray code transformation makes the bit states of cross points change from the original data bits, so firstly the probabilities of data bits on specific bit planes in cross-point regions and then the entropies of the messages are changed. These probabilities are estimated and compared with the probabilities of the original data bits. This change of probability has important effects on the encoding and decoding processes in lossless medical image compression.
机译:这项研究提出了用于在多位平面上进行无损图像压缩的交叉点区域(CRICM),该算法通过优化作为相邻点的交叉点在不同位平面上的位的概率来无损编码和解码图像(尤其是医学图像)的算法2 n 的灰度级。基于格雷编码,由于图像特征性地包含在特定区域内变化不大的数据,因此在相邻数据集上确定了交叉点的格雷码,然后将这种效果推广到了实际数据中,而不会失去其统计特性的一般性。对于具有许多具有相同灰度级的区域的医学图像而言尤其如此。格雷码变换使交叉点的位状态与原始数据位发生变化,因此,首先在交叉点区域中特定位平面上的数据位的概率,然后更改消息的熵。估计这些概率,并将其与原始数据位的概率进行比较。概率的这种变化对无损医学图像压缩中的编码和解码过程具有重要影响。

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