An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image
An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image
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一种基于 JPEG-XT 的优化算法,用于 16 位深度医学图像的有损和无损压缩
DOI:
10.1016/j.bspc.2020.102306
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发表时间:
2021
影响因子:
5.1
通讯作者:
Xu, Jian
中科院分区:
文献类型:
--
作者:
Li, Zhongqiang;Ramos, Alexandra;Li, Zheng;Osborn, Michelle L.;Li, Xin;Li, Yanping;Yao, Shaomian;Xu, Jian
JPEG-based compression is the most widely used image compression algorithm. Previously, JPEG-XT-based work focused on 16-bit depth high-dynamic satellite infrared images, but not on medical images. In this study, we represent an optimized JPEG-XT method (OPT_JPEG-XT) that better compresses 16-bit depth medical images by amplifying (N times) discrete cosine transform (DCT) coefficients. The results show that the small integers and the first two decimal portions of DCT coefficients play important roles in the compression of medical images. By using the appropriate N and number of decimal portions (NDP), OPT_JPEG-XT could realize lossless compression of medical images. Regarding upper and lower 8-bit subimages, the upper subimages have more important roles in the improvement of OPT_JPEG-XT performance than the lower subimages; lower subimages could occupy over 90% sizes of the entire encoding files. Thus, OPT_JPEG-XT could save about 60% of storage space with high PSNR (peak signal-noise-ratio, over 100) and low MSE (mean-square-error, less than 0.08) by decreasing the compression efficiency of lower subimages. Compared to the conventional JPEG-XT and JPEG 2000, OPT_JPEG-XT can acquire a similar compression performance (PSNR > 100, MSE < 0.9, SSR (saving space rate, >60%) to JPEG 2000 when using N = 20 for lower subimages and lossless compression of upper subimages. OPT_JPEG-XT could obtain high SSR (about 90%, similar to traditional JPEG-XT) with with much smaller MSE (25 times lower than conventional JPEG-XT). Therefore, OPT_JPEG-XT could be developed a novel compression method that could realize the lossless and lossy compression of medical images.
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影响因子:
2.9
作者:
Jian Xu;D. Kooby;S. Nie
通讯作者:
S. Nie
DOI:
--
发表时间:
--
期刊:
影响因子:
--
作者:
Seungcheol Choi;Oh;Dukhyun Jang
通讯作者:
Dukhyun Jang
DOI:
--
发表时间:
2015
期刊:
影响因子:
--
作者:
T. Kumar;D. Kumar
通讯作者:
D. Kumar
DOI:
--
发表时间:
2004
期刊:
Proceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering
影响因子:
--
作者:
Yen;S. Tai
通讯作者:
S. Tai
影响因子:
3
作者:
Ketschek A
通讯作者:
Ketschek A