Hewlett-packard Development Company, L.P.

Hewlett-packard Company

Other

Hewlett Packard Enterprise Development LP

Marcelo Weinberger

Gadiel Seroussi

Erik Ordentlich

Sergio Verdu

Itschak Weissman

Giovanni Motta

Guillermo Sapiro

John George Apostolopoulos

Krishnamurthy Viswanathan

Raul Hernan Etkin

Alvaro Martin

Michael Baer

Gadlel Seroussi

Ignacio Ramirez

Tsachy Weissman

Rajiv Gupta

Raul Herman Etkin

Neri Merhav

Tomas G Rokicki

Erik Ordentilch

Krishnamurthy Vlswanathan

Itschak (Tsachy) Weissman

Ignaclo Ramirez

George Gemelos

Sergio Verdú

Adriana Piazza

Joesp M Ferrandiz

Erik Ordenllich

Raul Heman Etkin

Cheng Chang

A distinguished node is dynamically selected from a subset of nodes in a wireless network. Data samples from the subset of nodes are received in view of the distinguished node status. At least one estimate is generated from the data samples and the data samples are compressed conditioned on the estimate.

Compressing data in a wireless network

A joint image compression system and method compress a target image and a reference image under a selected transform to produce a compressed difference image. The joint image compression system includes a computer readable media and a computer program stored on the computer readable media. The computer program includes instructions that implement selecting a transform from among a plurality of transforms that includes a subset determined projective (SDP) transform, where the selected transform minimizes a cumulative mapping error (CME) for corresponding feature points in each of a target image and a reference image. The instructions further implement applying the selected transform to one of the target image and the reference image; forming a difference image under the selected transform and compressing the difference image to produce a compressed difference image.

Joint image compression method and apparatus

One embodiment of the present invention is directed to a method for compressing data generated by multiple data sources. The method includes steps of partitioning data generated by the multiple data sources into data partitions, the data included in each data partition containing inter-data-source redundancies and, for each data partition, compressing the data in the data partition to remove the inter-data-source redundancies.

Multiple-source data compression

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 zof 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.

Method and system for adaptive context-embedded prediction

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. Each iteration of the iterative methods applies context-based denoising only for those conditioning classes that statistical analysis indicates remain valid for denoising purposes.

Context-cluster-level control of filtering iterations in an iterative discrete universal denoiser

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.

Method and system for robust universal denoising of noisy data sets

One embodiment of the present invention is a sensor comprising one or more sensing devices, data-transmission components that transmit sensor data to a receiving component, and a processing component. The processing component executes routines to record sensing-device output as data for transmission to the receiving entity and to control the data-transmission components to transmit the data to the receiving entity. The processing component executes one or more compressing routines to compress data prior to transmission, when data compression is estimated to result in a lower power cost than transmitting uncompressed data, and controlling the data-transmission components to transmit data without compressing the data when data compression is estimated to result in a higher power cost than transmitting uncompressed data.

Method and system for low-powered data transmission

A first node receives aggregated compressed data and unaggregated data from a second node in a wireless multi-hop network. The first node compresses its own collected data based on the received unaggregated data. The first node aggregates its own compressed data with the aggregated compressed data received from the second node. The first node forwards an unaggregated version of its own collected data along with aggregated compressed data to a next hop in the wireless multi-hop network.

Compressing data in a wireless multi-hop network

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.

Denoising and error correction for finite input, general output channel

Various embodiments of the present invention relate to a discrete denoiser that replaces symbols in a received, noisy signal with replacement symbols 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. Denoisers that represent examples of the present invention use blended counts that are combinations of the occurrences of metasymbol families within a noisy signal to determine the symbols to be replaced and the replacement symbols corresponding to them.

Discrete denoising using blended counts

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.

Method and system for determining an optimal or near optimal set of contexts by constructing a multi-directional context tree

Systems and methods for iterative denoising and error correction decoding are presented. In an embodiment, a system iteratively processes a noisy signal formed by a noise-introducing channel in response to an error correction coded input signal. A denoiser generates reliability information for symbols in the input signal. The reliability information is generated by forming a product of received reliability information and an estimate of the distribution of metasymbols in the input signal, each metasymbol includes context symbols and a corresponding input symbol, and summing over values for the context symbols corresponding to the particular input symbol. An error correction decoder performs error correction decoding using the generated reliability information. In another embodiment, the reliability information is generated by forming a hard-decision decoded signal from received reliability information, determining metasymbol counts from the hard-decision decoded signal and solving an equation whose coefficients are obtained from the received reliability information and the metasymbol counts.

System and method for iterative denoising and error correction decoding

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.

Universal-denoiser context-modeling component and context-modeling method

A method and apparatus for processing a received digital signal that has been corrupted by a channel is disclosed. The method includes storing the received digital signal and receiving a partially corrected sequence of symbols that includes an output of a preliminary denoising system operating on the received digital signal. Information specifying a signal degradation function that measures the signal degradation that occurs if a symbol having the value I is replaced by a symbol having the value J is utilized to generate a processed digital signal by replacing each symbol having a value I in a context of that symbol in the received digital signal with a symbol having a value J if replacement reduces a measure of overall signal degradation in the processed digital signal relative to the received digital signal as measured by the degradation function and the partially corrected sequence of symbols.

Enhanced denoising system

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.

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San Jose, CA, United States of America

Marcelo Weinberger