Abstract
This paper presents a new context formation and lossless compression of medical images in which has huge number of pixels and 2-byte pixel depth. We analyze various prediction techniques and compare their performance. The initial prediction is used for the context to update and correct the prediction error. The results show that diagonal edge detection-based prediction does not perform well in medical images and the proposed scheme outperforms JPEG-LS and DMED in terms of compression ratio up to 2.2%. © Springer-Verlag 2003.
Cite
CITATION STYLE
Hwang, J. J., Cho, S. G., Hwang, C. G., & Lee, J. S. (2004). Prediction error context-based lossless compression of medical images. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2690, 1052–1055. https://doi.org/10.1007/978-3-540-45080-1_149
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