This paper compares the signal-to-noise ratio performance of several polynomial interpolator algorithms. Signal-to-noise analysis is extended to the improvement thereof as the input signal is ideally oversampled. The algorithms considered are drop-sample, linear, third-order Lagrange, Hermite, second-order osculating, and B-spline. The analysis reveals an interesting performance trade-off between signal-to-noise ratio and passband flatness.
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