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244 results about "Adaptive compression" patented technology

Adaptive compression is a type of data compression which changes compression algorithms based on the type of data being compressed.

Adaptive compression and decompression of bandlimited signals

An efficient method for compressing sampled analog signals in real time, without loss, or at a user-specified rate or distortion level, is described. The present invention is particularly effective for compressing and decompressing high-speed, bandlimited analog signals that are not appropriately or effectively compressed by prior art speech, audio, image, and video compression algorithms due to various limitations of such prior art compression solutions. The present invention's preprocessor apparatus measures one or more signal parameters and, under program control, appropriately modifies the preprocessor input signal to create one or more preprocessor output signals that are more effectively compressed by a follow-on compressor. In many instances, the follow-on compressor operates most effectively when its input signal is at baseband. The compressor creates a stream of compressed data tokens and compression control parameters that represent the original sampled input signal using fewer bits. The decompression subsystem uses a decompressor to decompress the stream of compressed data tokens and compression control parameters. After decompression, the decompressor output signal is processed by a post-processor, which reverses the operations of the preprocessor during compression, generating a postprocessed signal that exactly matches (during lossless compression) or approximates (during lossy compression) the original sampled input signal. Parallel processing implementations of both the compression and decompression subsystems are described that can operate at higher sampling rates when compared to the sampling rates of a single compression or decompression subsystem. In addition to providing the benefits of real-time compression and decompression to a new, general class of sampled data users who previously could not obtain benefits from compression, the present invention also enhances the performance of test and measurement equipment (oscilloscopes, signal generators, spectrum analyzers, logic analyzers, etc.), busses and networks carrying sampled data, and data converters (A/D and D/A converters).
Owner:TAHOE RES LTD

Lempel-Ziv data compression technique utilizing a dictionary pre-filled with frequent letter combinations, words and/or phrases

An adaptive compression technique which is an improvement to Lempel-Ziv (LZ) compression techniques, both as applied for purposes of reducing required storage space and for reducing the transmission time associated with transferring data from point to point. Pre-filled compression dictionaries are utilized to address the problem with prior Lempel-Ziv techniques in which the compression software starts with an empty compression dictionary, whereby little compression is achieved until the dictionary has been filled with sequences common in the data being compressed. In accordance with the invention, the compression dictionary is pre-filled, prior to the beginning of the data compression, with letter sequences, words and/or phrases frequent in the domain from which the data being compressed is drawn. The letter sequences, words, and/or phrases used in the pre-filled compression dictionary may be determined by statistically sampling text data from the same genre of text. Multiple pre-filled dictionaries may be utilized by the compression software at the beginning of the compression process, where the most appropriate dictionary for maximum compression is identified and used to compress the current data. These modifications are made to any of the known Lempel-Ziv compression techniques based on the variants detailed in 1977 and 1978 articles by Ziv and Lempel.
Owner:PINPOINT

Adaptive compressed sensing-based non-local reconstruction method for natural image

The invention discloses an adaptive compressed sensing-based non-local reconstruction method for a natural image. The problems of serious reconstructed image information loss and the like in the prior art are mainly solved. The method is implemented by the steps of: (1) dividing an image into N 32*32 sub-blocks, obtaining a basic sensing matrix Phi' according to a basic sampling rate b and a sensing matrix Phi, and sampling a signal by utilizing Phi' to obtain a basic observation vector; (2) estimating a standard deviation sequence {d1, d2, ..., and dN} of the image according to the basic observation vector; (3) adaptively allocating a sampling rate ai for each sub-block according to the standard deviation sequence {d1, d2, ..., and dN}, and constructing an adaptive sensing matrix, and sampling the signal by utilizing the adaptive sensing matrix to obtain an adaptive observation vector; (4) forming an observation vector of each sub-block by using the basic observation vector and the adaptive observation vector; (5) obtaining an initial solution x0 of the image according to the observation vector; and (6) performing iteration by using x0, and reconstructing the original image until consistency with a finishing condition is achieved to obtain a reconstructed image x'. The method has the advantages of high image reconstruction quality, clear principle and operational simplicity, and is applied to the sampling and reconstruction of the natural image.
Owner:XIDIAN UNIV

Hearing aids based on models of cochlear compression using adaptive compression thresholds

A hearing aid device providing instantaneous gain compression for sound signals and adaptive control of nonlinear waveform distortion, the device comprising: (a) at least one bandpass nonlinearity (BPNL) amplifier comprising a first bandpass filter, a second bandpass filter, and a memoryless nonlinear (MNL) compressive audio amplifier configured to receive a sound signal from the first bandpass filter and provide an MNL compressive audio amplifier output to the second bandpass filter, wherein the MNL compressive audio amplifier is configured to produce the MNL compressive audio amplifier output by providing memoryless gain compression directly on a sound signal that is (1) received from the first bandpass filter and (2) exhibits instantaneous amplitudes greater than a compression threshold, the BPNL amplifier thereby producing a desired gain compression on the received sound signal at an output of the second bandpass filter, and (b) a controller in communication with the BPNL amplifier, the controller being configured to adjust the compression threshold of the MNL compressive audio amplifier. Adjustment of the compression threshold in each BPNL amplifier may be achieved at least partially in response to a user input and / or to sound signal changes. By adaptively controlling the compression threshold, performance of the device can by optimized to match its environment.
Owner:HEARING EMULATIONS

Self-adaptation compression reconstruction method based on energy effectiveness observation in cognitive sensor network

InactiveCN103347268AConsider energy efficiencyTaking into account effectivenessNetwork topologiesHigh level techniquesAdaptive compressionNODAL
The invention discloses a self-adaptation compression reconstruction method based on energy effectiveness observation in a cognitive sensor network. The self-adaptation compression reconstruction method based on the energy effectiveness observation in the cognitive sensor network comprises the steps that (1) a node carries out local detection and compression measurement on data which are actually sensed through an analog transcriber according to the characteristic that power consumption of the node of a cognitive sensor is limited, (2) a space-time relevance structure of sensing signals is used, sensing data are mapped to a wavelet orthogonal basis cascading dictionary to carry out sparse conversion and to carry out self-adaptation observation through a weighting energy subset function, appropriate observation values are obtained in an energy effectiveness mode, orthogonalization is carried out on selected observation vectors to construct a measurement matrix, (3) the sensing data after compression measurement are fed back to an aggregation node through a report channel, the aggregation node carries out self-adaptation reconstruction on the sensing data by using a gradient projection sparse reconstruction Barzilai-Borwein method based on a convex relaxation method, and effective compromise between reconstruction performance and energy consumption of the node is achieved. The self-adaptation compression reconstruction method based on the energy effectiveness observation in the cognitive sensor network can carry out accurate reconstruction on the sensing signals, ensures energy effectiveness of the sensing node, and has actual application significance.
Owner:HANGZHOU DIANZI UNIV

Multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method

The invention discloses a multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method, which relates to the technical field of information and communication. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method is provided for solving the problem of recovering an original multiband signal from multiple observed value vectors with unknown sparsity after continuous-limited module conversion through sampling by a modulated broadband converter under an Xampling framework. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method comprises the steps of: conducting self-adaptive estimation on sparsity of a signal; updating the sparsity with a given step length factor through repeated iteration so that the sparsity gradually approaches the actual sparsity of the signal; correcting a support set through a backtracking thought and a minimum mean square criterion; stopping iteration until an residual error is less than a set threshold value; and finally reconstructing an original multiband signal through pseudo inverse operation by utilizing the obtained complete support set. The multiple observed value vector sparsity self-adaptive compressed sampling matching pursuit method can achieve the analog reconstruction of the multiband signal based on compressed sensing.
Owner:HARBIN INST OF TECH
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