Sharper bounds on Occam filters and application to digital video

Occam filters are a general technique for filtering random noise via data compression. Previously it was established that these filters converge in a learning theoretic sense, with convergence bounds that depended on the probability distribution of the noise variable. The paper presents a convergence bound for uniformly sampled signals that is independent of the probability distribution of the noise variable, barring some minimal assumptions. It also examines an application of Occam filters to remove random noise from digital video, thereby enabling improved nearly lossless compression