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清华大学 [1]
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长春光学精密机械与物... [1]
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基于项目驱动的XML课程教学方案设计
期刊论文
2011
陈荣鑫
;
江边
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浏览/下载:1/0
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提交时间:2016/05/17
XML课程
项目驱动
教学方案设计
XML course
project driven
teaching scheme design
重新审视和设计中国高校会计第一门课
期刊论文
2010, 2010
赵晓东
;
王竞达
;
ZHAO Xiao-dong
;
WANG Jing-da
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浏览/下载:2/0
Lossless wavelet compression on medical image (EI CONFERENCE)
会议论文
4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China
Zhao X.
;
Wei J.
;
Zhai L.
;
Liu H.
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浏览/下载:29/0
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提交时间:2013/03/25
An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image
thus facilitating accurate diagnosis
of course at the expense of higher bit rates
i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization
wavelet coding
neural networks
and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1
or even more)
they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image
but the achievable compression ratios are only of the order 2:1
up to 4:1. In our paper
we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time
we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance
so that all the low rate codes are included at the beginning of the bit stream. Typically
the encoding process stops when the target bit rate is met. Similarly
the decoder can interrupt the decoding process at any point in the bil stream
and still reconstruct the image. Therefore
a compression scheme generating an embedded code can start sending over the network the coarser version of the image first
and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.
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