Abstract: Embodiments of the present invention are directed to various enhanced discrete-universal denoisers that have been developed to denoise images and other one-dimensional, two-dimensional or higher-dimensional data sets in which the frequency of occurrence of individual contexts may be too low to gather efficient statistical data or context-based symbol prediction. In these denoisers, image quality, signal-to-noise ratios, or other measures of the effectiveness of denoising that would be expected to increase monotonically over a series of iterations may decrease, due to assumptions underlying the discrete-universal-denoising method losing validity. Embodiments of the present invention apply context-class-based statistics and statistical analysis to determine, on a per-context-class basis, when to at least temporarily terminate denoising iterations on each conditioning class.

Abstract: Embodiments of the present invention provide context-class-based universal denoising of noisy images and other noise-corrupted data sets. Prediction-error statistics for each prediction class, relative to a prefiltered image, are collected to estimate a bias for each prediction class, and prediction-error statistics for each conditioning class, relative to a prefiltered image, are accumulated based on the difference between predicted values and corresponding prefiltered-image symbols. The prediction-error statistics are accumulated using computed prediction-error-statistics vectors, with inversion of a prediction-error vector generated from each prediction prior to accumulation in a prediction-error-statistics vector. Conditional probability distributions are computed for individual contexts, which allow for computing a clean-image-estimated, value for each noisy-image value by minimizing a computed distortion over a range of possible estimated-clean-image symbols.

Abstract: One embodiment of the present invention is directed to an adaptive context-based predictor that predicts a value {circumflex over (x)} from a context, stored in an electronic memory, corresponding to a noisy-dataset symbol zi of a noisy dataset corrupted with noise modeled as being introduced by a noise-introducing channel. The adaptive context-based predictor is adapted according to one or more parameters that specify adaptive context-based-predictor operation, at least one of which functionally depends, or partially functionally depends, on a level of noise represented by the noise-introducing channel. The adaptive context-based predictor computes a number of intermediate values from the context, computes the predicted value {circumflex over (x)} from the intermediate values, and stores the predicted value {circumflex over (x)} in the electronic memory.

Abstract: In various embodiments of the present invention, a binary mask corresponding to a noisy symbol sequence is produced to indicate which of the symbols in the noisy symbol sequence has potentially been modified, or altered, by a noisy channel. DUDE, DUDE-CTI, and other denoising methods are modified to employ the bit mask in order to avoid the computational overhead and potential errors incurred in attempting to denoise symbols that are not likely to have been altered by the noisy channel.

Abstract: A denoising process models a noisy signal using classes and subclasses of symbol contexts. The process generates class count vectors having components that combine occurrence counts for different symbols in different contexts. Biases determined separately for each subclass and a fixed predictor indicate which symbol occurrence counts for different context are combined in the same component of a class count vector. For impulse noise, the bias for a subclass can be the average error that results when the fixed predictor predicts non-noisy symbols found in contexts of the context subclass. Denoising of impulse noise can select replacement symbols without matrix multiplication or a channel matrix inverse by evaluating distributions that result from subtracting error probabilities from probability vectors associated with respective contexts. Probability mass can be moved from adjacent components of the probability vector to assure that subtraction of the error probabilities leaves non-negative results.

Abstract: In a method of compressing a data sequence, the data sequence is parsed into data segments, where at least one of the data segments includes a match. In addition, the match is compressed using at least one context model that depends upon one or more coded data symbols that are available to a decoder. An encoder includes a coding unit configured to code at least one of a match offset and a match length of a data segment using one or more context models that depend on previously decodeable data symbols. A computer readable storage medium having a computer program for implementing the method of compressing the data sequence.

Abstract: Various embodiments of the present invention provide methods and systems for determining, representing, and using variable-length contexts in a variety of different computational applications. In one embodiment of the present invention, a balanced tree is used to represent all possible contexts of a fixed length, where the depth of the balanced tree is equal to the fixed length of the considered contexts. Then, in the embodiment, a pruning technique is used to sequentially coalesce the children of particular nodes in the tree in order to produce an unbalanced tree representing a set of variable-length contexts. The pruning method is selected, in one embodiment, to coalesce nodes, and, by doing so, to truncate the tree according to statistical considerations in order to produce a representation of a variably sized context model suitable for a particular application.

Abstract: A denoising process or system uses convex optimization to determine characteristics of a clean signal. In one embodiment, a noisy signal that represents a set of symbols can be scanned to determine an empirical vector with components respectively indicating respective empirical probabilities of symbols in the noisy signal that occur in a particular context. A convex optimization process can then identify a vector such that a difference between the empirical vector and a product of the identified vector and a channel matrix is minimized. The identified vector can be used to determine when a symbol in the noisy signal should be replaced when assembling a reconstructed signal.

Abstract: In a method of compressing a data sequence, the data sequence is parsed into data segments, where at least one of the data segments includes a match. In addition, the match is compressed using at least one context model that depends upon one or more coded data symbols that are available to a decoder. An encoder includes a coding unit configured to code at least one of a match offset and a match length of a data segment using one or more context models that depend on previously decodeable data symbols. A computer readable storage medium having a computer program for implementing the method of compressing the data sequence.

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.

Abstract: Systems and methods are disclosed for denoising for a finite input, general output channel. In one aspect, a system is provided for processing a noisy signal formed by a noise-introducing channel in response to an error correction coded input signal, the noisy signal having symbols of a general alphabet. The system comprises a denoiser and an error correction decoder. The denoiser generates reliability information corresponding to metasymbols in the noisy signal based on an estimate of the distribution of metasymbols in the input signal and upon symbol transition probabilities of symbols in the input signal being altered in a quantized signal. A portion of each metasymbol provides a context for a symbol of the metasymbol. The quantized signal includes symbols of a finite alphabet and is formed by quantizing the noisy signal. The error correction decoder performs error correction decoding on noisy signal using the reliability information generated by the denoiser.

Abstract: Various embodiments of the present invention provide a compression method and system that compresses received data by first denoising the data and then losslessly compressing the denoised data. Denoising removes high entropy features of the data to produce lower entropy, denoised data that can be efficiently compressed by a lossless compression technique. One embodiment of the invention is a universal lossy compression method obtained by cascading a denoising technique with a universal lossless compression method. Alternative embodiments include methods obtained by cascading a denoising technique with one or more lossy or lossless compression methods.

Abstract: In various embodiments of the present invention, a noisy signal denoiser is tuned and optimized by selecting denoiser parameters that provide relatively highly compressible denoiser output. When the original signal can be compared to the output of a denoiser, the denoiser can be accurately tuned and adjusted in order to produce a denoised signal that resembles as closely as possible the clear signal originally transmitted through a noise-introducing channel. However, when the clear signal is not available, as in many communications applications, other methods are needed. By adjusting the parameters to provide a denoised signal that is globally or locally maximally compressible, the denoiser can be optimized despite inaccessibility of the original, clear signal.

Abstract: In various embodiments of the present invention, a context-based denoiser is applied to each noisy-image symbol embedded within a context to determine a replacement symbol for the noisy-signal symbol. The context-based denoiser includes a context-modeling component that efficiently generates context classes and symbol-prediction classes, assigns individual contexts to context classes and symbol-prediction classes, collects symbol-occurrence statistics related to the generated context classes and symbol-prediction classes, and, optionally, generates noisy-symbol predictions.

Abstract: An image is compressed by selectively performing at least one of palettization and interframe coding on certain regions of the image. The regions are adaptively determined.

Abstract: Systems and methods are disclosed for denoising for a finite input, general output channel. In one aspect, a system is provided for processing a noisy signal formed by a noise-introducing channel in response to an error correction coded input signal, the noisy signal having symbols of a general alphabet. The system comprises a denoiser and an error correction decoder. The denoiser generates reliability information corresponding to metasymbols in the noisy signal based on an estimate of the distribution of metasymbols in the input signal and upon symbol transition probabilities of symbols in the input signal being altered in a quantized signal. A portion of each metasymbol provides a context for a symbol of the metasymbol. The quantized signal includes symbols of a finite alphabet and is formed by quantizing the noisy signal. The error correction decoder performs error correction decoding on noisy signal using the reliability information generated by the denoiser.

Abstract: An apparatus for operating on a received signal that includes a noise-free signal that has been corrupted by a channel is disclosed. A memory stores a channel corruption function specifying the probability that a symbol having a value I was converted to a symbol having a value J by the channel, and a degradation function measuring the signal degradation that occurs if a symbol having the value I is replaced by symbol having a value J. The controller parses one of the received signal or the processed signal into phrases, and replaces one of the symbol having a value I in a context of that symbol in the received signal with a symbol having a value J if the replacement would reduce the estimated overall signal degradation in the processed signal. The context of a symbol depends on the phrase associated with the symbol.

Abstract: Use of Generalized Context Trees to assign a unique state from a finite set to any string is provided. The method optionally refines the generalized context tree into a refined generalized context tree having a finite state machine (FSM) property. Refining occurs whenever the generalized context tree does not have the finite state machine property. Alternately, a method for constructing a representation of a source usable within an FSM is provided, comprising evaluating a node comprising a suffix tail and verifying the suffix tail is included in the representation, and inserting at least one node to the representation when the suffix tail is not in the representation.

Abstract: A denoising process statistically processes a series of frames of a motion picture to construct respective data structures for the frames. Each data structure indicates for each of multiple contexts, occurrences of symbols that have the same context and are in the corresponding one of the frames. The data structures for multiple frames are combined to construct an enhanced data structure for one of the frames, and symbols in that frame are replaced with values determined using the enhanced data structure.

Abstract: In various embodiments of the present invention, a binary mask corresponding to a noisy symbol sequence is produced to indicate which of the symbols in the noisy symbol sequence has potentially been modified, or altered, by a noisy channel. DUDE, DUDE-CTI, and other denoising methods are modified to employ the bit mask in order to avoid the computational overhead and potential errors incurred in attempting to denoise symbols that are not likely to have been altered by the noisy channel.