The Experts below are selected from a list of 35124 Experts worldwide ranked by ideXlab platform
Shaomian Yao - One of the best experts on this subject based on the ideXlab platform.
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an optimized jpeg xt based algorithm for the lossy and lossless compression of 16 Bit depth medical image
Biomedical Signal Processing and Control, 2021Co-Authors: Alexandra Ramos, Michelle L Osborn, Shaomian YaoAbstract:Abstract 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 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.
Alexandra Ramos - One of the best experts on this subject based on the ideXlab platform.
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an optimized jpeg xt based algorithm for the lossy and lossless compression of 16 Bit depth medical image
Biomedical Signal Processing and Control, 2021Co-Authors: Alexandra Ramos, Michelle L Osborn, Shaomian YaoAbstract:Abstract 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 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.
Michelle L Osborn - One of the best experts on this subject based on the ideXlab platform.
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an optimized jpeg xt based algorithm for the lossy and lossless compression of 16 Bit depth medical image
Biomedical Signal Processing and Control, 2021Co-Authors: Alexandra Ramos, Michelle L Osborn, Shaomian YaoAbstract:Abstract 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 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.
