{"title":"A lossless coding scheme for images by using Cross point Regions on Multiple bit planes","authors":"T. T. Dang, S. K. Nguyen, T. Vu, S. Higuchi","doi":"10.1109/IST.2009.5071650","DOIUrl":null,"url":null,"abstract":"This paper presents CRICM (Cross point Regions for lossless Image Compression on Multiple bit planes), an algorithm for losslessly encoding and decoding images, especially medical images, by optimizing on the probability of bits on different bit planes of cross points that are neighbor points of grey levels 2″. Based on Gray coding, Gray codes of cross points are determined on an adjacent data set because images characteristically contain data that does not change much in a specific area; then this effect is generalized 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 first 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.","PeriodicalId":373922,"journal":{"name":"2009 IEEE International Workshop on Imaging Systems and Techniques","volume":"142 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Workshop on Imaging Systems and Techniques","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IST.2009.5071650","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents CRICM (Cross point Regions for lossless Image Compression on Multiple bit planes), an algorithm for losslessly encoding and decoding images, especially medical images, by optimizing on the probability of bits on different bit planes of cross points that are neighbor points of grey levels 2″. Based on Gray coding, Gray codes of cross points are determined on an adjacent data set because images characteristically contain data that does not change much in a specific area; then this effect is generalized 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 first 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.
本文提出了crims (Cross point Regions for loss - less Image Compression on Multiple bit plane)算法,该算法通过优化灰度值为2的相邻交叉点在不同位平面上的比特的概率,实现图像,特别是医学图像的无损编码和解码。″基于灰度编码,在相邻数据集上确定交叉点的灰度编码,因为图像通常包含在特定区域内变化不大的数据;然后将这种效应推广到实际数据中,同时又不失其统计性质的一般性。对于具有许多具有相同灰度级别的区域的医学图像尤其如此。Gray码变换使交叉点的位状态从原始数据位改变,首先改变交叉点区域内特定位平面上数据位的概率,然后改变信息的熵。对这些概率进行估计,并与原始数据位的概率进行比较。这种概率的变化对医学图像无损压缩的编码和解码过程有着重要的影响。