{"title":"Progressive near-lossless coding of medical images","authors":"A. Krivoulets","doi":"10.1109/ISPA.2003.1296894","DOIUrl":null,"url":null,"abstract":"We propose an algorithm intended for compression of medical images, which allows for embedded coding in L/sub /spl infin// sense, i.e., progressive near-lossless as well as lossless image compression. The method is based on a lossy plus near-lossless layered compression scheme and embedded quantization of the difference signal. We show, that this technique allows for a better image quality and compression performance for large tolerance values than algorithms based on predictive coding. The lossy plus near-lossless scheme also allows for image reconstruction with a minimum mean square error (MSE) criterion, while providing a strict control of the maximum absolute difference error. This property is impossible in predictive coding algorithms.","PeriodicalId":218932,"journal":{"name":"3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPA.2003.1296894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

We propose an algorithm intended for compression of medical images, which allows for embedded coding in L/sub /spl infin// sense, i.e., progressive near-lossless as well as lossless image compression. The method is based on a lossy plus near-lossless layered compression scheme and embedded quantization of the difference signal. We show, that this technique allows for a better image quality and compression performance for large tolerance values than algorithms based on predictive coding. The lossy plus near-lossless scheme also allows for image reconstruction with a minimum mean square error (MSE) criterion, while providing a strict control of the maximum absolute difference error. This property is impossible in predictive coding algorithms.
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渐进式医学图像近无损编码
我们提出了一种用于医学图像压缩的算法,该算法允许在L/sub /spl infin// sense中嵌入编码,即渐进式近无损和无损图像压缩。该方法基于有损加近无损的分层压缩方案和差分信号的嵌入量化。我们表明,与基于预测编码的算法相比,这种技术可以在大容差值下获得更好的图像质量和压缩性能。有损加近无损方案还允许图像重建与最小均方误差(MSE)标准,同时提供了严格的控制最大绝对差误差。这种特性在预测编码算法中是不可能的。
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