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Table 5 Rate and MSE obtained by running the algorithm proposed in [39] on the M. musculus dataset

From: QualComp: a new lossy compressor for quality scores based on rate distortion theory

LogBinning R MSE   UniBinning R MSE   Truncating R MSE
60 0 684.29   5 0.25 405.14   33 0.01 684.29
34 0.29 632.13   10 0.35 279.63   40 0.26 404.92
26 0.65 80.160   17 0.41 226.08   50 0.58 137.08
17 0.62 129.42   26 0.47 178.60   60 1.58 15.01
10 1.13 14.51   34 0.51 157.10   70 3.24 0.00
5 1.42 6.03   60 0.61 118.58     
     70 0.67 101.53     
     80 0.74 85.92     
     90 0.74 85.92     
     100 0.79 71.82     
     200 1.08 37.53     
  1. MSE obtained by the LogBinning, UniBinning and Truncating schemes proposed in [39] for different parameters.