Abstract: The present invention provides methods for universal lossy compression that provide performance at or near the rate-distortion limit and that are based on universal, implementable lossy source coding algorithms.

Abstract: The present invention provides methods for universal lossy compression that provide performance at or near the rate-distortion limit and that are based on universal, implementable lossy source coding algorithms.

Abstract: Denoising such as discrete universal denoising (DUDE) that scans a noisy signal in an attempt to characterize probabilities of finding symbol values in a particular context in a clean signal can perform a rough denoising on the noisy signal and identify contexts from a roughly denoised signal. The rough denoising improves estimation of the statistical properties of the clean signal by reducing the false differentiation of contexts that noise can otherwise create. Statistical information regarding occurrences of symbols in the noisy signal and corresponding contexts in the roughly denoised signal can then be used to denoise the noisy signal. The specifics of the rough denoising can be chosen based on knowledge of the noise or of the clean data. Alternatively, the DUDE can be used in an iterative fashion where the denoised signal produced from a prior iteration provides the contexts for the next iteration.