If you are using lossy compression – inevitable in projects like teleradiology – you will need to experiment with different compression settings provided in your software to see that the diagnostic integrity of your images remains intact. This leaves you with only one real option: trying it yourself. Any data for which the Comprestimator tool estimates 25 or higher savings. 2009), without any guarantee that the same R cwill work for you. Real-time compression implementation in XIV storage uses above cache. 2010) – always specific to the images, circumstances, experiences 7 or even countries (Table 4.2) (Loose et al. 2012), 6 while the others advise caution even at R c = 8 (Erickson et al. 2012), 70 (Peterson and Wolffsohn 2005), or even R c = 171 (Peterson et al. Most literature on this matter seems to be strangely attracted to R c = 10, but some suggest 15–20 (Kim et al. SPEC first segments a compound image into text/graphics pixels and pictorial pixels, and then compresses the text/graphics pixels with a new lossless coding. As a result, there is no universally-perfect ε for diagnostically-safe lossy compression, and there is no universally-perfect R cto ensure that your lossy image will stay flawless. Real-time image transmission requires that the compression algorithm should not only achieve high compression ratio, but also have low complexity and provide excellent visual quality. Moreover, pixel value fluctuations can depend on different tissue types, image acquisition protocols, imaging artifacts, and so forth. Consequently, the same values of ε can be too high for one image (or image area, or specific malignancy) and way too low for another. Suffice it to say that the differences between image pixel values can be diagnostic in some cases (such as tiny micro-calcifications in mammograms), and purely meaningless in others (such as noise in low-dose X-ray or CT images). This is exactly where lossy compression becomes more of an art.
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