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Abstract

In a system in which a non-random, noise-free signal is disturbed by random noise, one first determines a way to measure the difference between two signals, and one also selects a measure of complexity for signals. Based on a series of discrete values of the noisy signal, a compression processor generates a series of compressed signals representing the noisy signal, each within a corresponding error or loss tolerance of the discrete values of the noisy signal. An optimization processor applies the various loss tolerance values to the compression processor and then evaluates the relative complexity of the corresponding compressed signals. The optimization processor then determines an optimal knee point loss tolerance, below which the complexity of the compressed signals rises rapidly. For continued filtering of the noisy signal, the compression processor compresses the noisy signal using the optimal knee point loss tolerance. The optimally compressed signals are then passed to a decompression processor, which generates a filtered signal corresponding to the noisy signal. In certain embodiments, the optimization processor also controls the sampling rate of the noisy signal; this makes possible a reduction in the residual loss of the filtered signal.

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First Claim