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: 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: A discrete, universal denoising method is applied to a noisy signal for which the source alphabet is typically large. The method exploits a priori information regarding expected characteristics of the signal. In particular, using characteristics of a continuous tone image such as continuity and small-scale symmetry allows definition of context classes containing large numbers of image contexts having similar statistical characteristics. Use of the context classes allows extraction of more reliable indications of the characteristic of a clean signal.

Abstract: A method of and system for generating reliability information for a noisy signal received through a noise-introducing channel. In one embodiment, symbol-transition probabilities are determined for the noise-introducing channel. Occurrences of metasymbols in the noisy signal are counted, each metasymbol providing a context for a symbol of the metasymbol. For each metasymbol occurring in the noisy signal, reliability information for each possible value of the symbol of the metasymbol is determined, the reliability information representing a probability that the value in the original signal corresponding to the symbol of the metasymbol assumed each of the possible values. In another embodiment, error correction coding may be performed by adding redundant data to an original signal prior to transmission by the noise-introducing channel and performing error correction decoding after transmission.

Abstract: A method for encoding and decoding a sequence is provided. The method comprises searching a set of candidate trees varying in size for a tree T having a plurality of states. Tree T provides a structure that relatively minimizes code length of the sequence from among all the candidate trees. The method further comprises encoding data conditioned on the tree T, which may be a generalized context tree (GCT), using a sequential probability assignment conditioned on the states of the tree T. This encoding may use finite state machine (FSM) closure of the tree. Also provided are methods for decoding an encoded binary string when the encoded string includes a full tree or generalized context tree, as well as decoding an encoded string using incomplete FSM closure, incremental FSM, and suffix tree construction concepts.

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 various embodiments of the present invention, optimal or near-optimal multidirectional context sets for a particular data-and/or-signal analysis or processing task are determined by selecting a maximum context size, generating a set of leaf nodes corresponding to those maximally sized contexts that occur in the data or signal to be processed or analyzed, and then building up and concurrently pruning, level by level, a multidirectional optimal context tree constructing one of potentially many optimal or near-optimal context trees in which leaf nodes represent the context of a near-optimal or optimal context set that may contain contexts of different sizes and geometries. Pruning is carried out using a problem-domain-related weighting function applicable to nodes and subtrees within the context tree. In one described embodiment, a bi-directional context tree suitable for a signal denoising application is constructed using, as the weighting function, an estimated loss function.

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: Compression and reconstruction of a digital image are both performed by accessing a plurality of color caches corresponding to different chromatic contexts, selecting a color cache for a pixel value being processed, and using information in the selected color cache to predict a value for the pixel being processed.

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: An apparatus and method for processing a received signal that has been corrupted by a channel to generate a processed signal having less signal corruption than the received signal is disclosed. The apparatus stores the received signal, information specifying the probability that a symbol having a value I will be converted to a symbol having a value J by the channel, and information specifying a signal degradation function that measures the signal degradation that occurs if a symbol having the value I is replaced by symbol having a value J. The controller replaces each symbol having a value I in a context of that symbol in the received signal with a symbol having a value J that minimizes the overall signal degradation in the processed signal relative to the underlying noise-free signal as estimated via the observed statistics within that context.

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: A discrete, universal denoising method is applied to a noisy signal for which the source alphabet is typically large. The method exploits a priori information regarding expected characteristics of the signal. In particular, using characteristics of a continuous tone image such as continuity and small-scale symmetry allows definition of context classes containing large numbers of image contexts having similar statistical characteristics. Use of the context classes allows extraction of more reliable indications of the characteristic of a clean signal.

Abstract: A number of methods and systems for efficiently storing defective-memory-location tables. A asymmetrical-distortion-model vector quantization method and a run-length quantization method for compressing a defective-memory-location bit map that identifies defective memory locations within a memory are provided. In addition, because various different compression/decompression methods may be suitable for different types of defect distributions within a memory, a method is provided to select the most appropriate compression/decompression method from among a number of compression/decompression methods as most appropriate for a particular defect probability distribution. Finally, bit-map compression and the figure-of-merit metric for selecting an appropriate compression technique may enable global optimization of error-correcting codes and defective memory-location identification.

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: Various embodiments of the present invention are directed to analysis of biopolymer sequences by introducing artificial noise into the sequences and then applying a denoiser to remove the artificial noise, monitoring the denoisability of each portion of the sequence by comparing the product of the denoiser and the original sequence. Portions of biopolymer sequences involved in certain cellular functions, such as genes within DNA sequences, often encode information in codes that are highly resilient to discrete, local corruption, such as DNA sequence mutations. Portions of DNA involved in other types of cellular functions may be less resilient to random errors, or, in other cases, may be so uniformly repetitive in sequence that random errors can be extremely easily identified and corrected. The denoisability of portions of biopolymer sequences into which random errors are introduced may thus rather directly reflect the error tolerance and error recognizability within the portions of biopolymer sequences.

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: Various embodiments of the present invention relate to a discrete denoiser that replaces all of one type of symbol in a received, noisy signal with a replacement symbol in order to produce a recovered signal less distorted with respect to an originally transmitted, clean signal than the received, noisy signal. Certain, initially developed discrete denoisers employ an analysis of the number of occurrences of metasymbols within the received, noisy signal in order to select symbols for replacement, and to select the replacement symbols for the symbols that are replaced. Embodiments of the present invention use blended counts that are combinations of the occurrences of metasymbol families within a noisy signal, rather than counts of individual, single metasymbols, to determine the symbols to be replaced and the replacement symbols corresponding to them.

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 method of and system for denoising and decoding a noisy error correction coded signal received through a noise-introducing channel to produce a recovered signal. In one embodiment, noisy message blocks are separated from noisy check blocks in the noisy error correction coded signal. The noisy message blocks are denoised. Error correction decoding is performed on the denoised message blocks using the noisy check blocks to produce the recovered signal.