Abstract
Some 20 years ago, when I was starting my student research in image compression, my more advanced friends kept asking me, why on earth I chose to burden myself with such an antiquated and useless subject. Disk storage was growing rapidly, network bandwidth was rising, and dialup modems were firing data with “lightning-fast” 14 kb/s. Why compress, when we’ll be able to handle everything uncompressed in a year?
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Notes
- 1.
If we need to account for the spaces between the letters as well, then we get 23/5 = 4.6 compression.
- 2.
- 3.
See LZW on Wikipedia for a good example of its encoding.
- 4.
- 5.
You might have noticed that some compression software will offer you “normal” and “best” compression options, meaning that “best” will employ a more extensive search for redundant pieces.
- 6.
Apart from compression, the authors also used several image format conversion steps (DICOM, BMP, AVI) which could have made image quality even worse.
- 7.
I can tell from my own experience that older radiologists seem to be more tolerant to image artifacts, and some publications confirm this point (Erickson et al. 2010).
- 8.
Don’t ask your software vendor – why would they care?
- 9.
In essence, DCT is the “cosine” part of the Fourier transform – thanks to Jean Baptiste Fourier, brave Napolean officer and governor of Low Egypt. See more at http://en.wikipedia.org/wiki/Discrete_cosine_transform
- 10.
Picture Archiving and Communication System, major application for working with diagnostic images.
- 11.
The CAD was used to detect lung nodules.
- 12.
Can be 3D and higher for volumetric and more complex imaging data.
- 13.
Contrast this with RLE compression, which simply assumes that the next pixel in line has the same value as its predecessor.
- 14.
References
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Pianykh, O.S. (2014). Image Compression. In: Digital Image Quality in Medicine. Understanding Medical Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-01760-0_4
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