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Perceptually lossless medical image coding
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AI
DOI：10.1109/TMI.2006.870483.png)
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摘要
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A novel perceptually lossless coder is presented for the compression of medical images. Built on the JPEG 2000 coding framework, the heart of the proposed coder is a visual pruning function, embedded with, an advanced human vision model to identify and to remove visually insignificant/irrelevant information. The proposed coder offers the advantages of simplicity and modularity with bit-stream compliance. Current results have shown superior compression ratio gains over that of its information lossless counterparts without any visible distortion. In addition, a case study consisting of 31 medical experts ha shown that no perceivable difference of statistical significance exists between the original images and the images compressed by the proposed coder.
Keyword:
biomedical imaging
double blind testing
image coding
just-not-noticeable- difference
medical image coding
perceptually lossless image coding
2-staged forced choice
vision model
期刊
IF:
9.8
论文数:
6.2K
被引数:
3.7W
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