105 results about "Vector quantisation" patented technology

Vector quantization provides font contour data reduction. Characters are defined in a font collection using segmented outline forms and common references stored to curve segments for similar segments in different characters of the font collection. Compressing the font collection preferably includes applying an entropy encoding data reduction. The common references are stored in a segment storage location. Another aspect includes decompressing a font store containing a compressed collection of outline fonts. The uncompressed curve segments are scaled so as to match a requested size for a letterform.

The invention relates to a speech coding method based on multiple description of grid vector quantization, which belongs to the field of communication technology and is mainly applied for group speech transmission. The method first of all conducts LPC analysis on speech signal, carries out perception weighting and transfers the weighted speech signal to a frequency domain. The signal is decomposed into vector in the frequency domain and is quantized with grid vector quantization method. Finally, the quantized grid point is decomposed into two descriptions (index of corresponding sub-gird point) according to the marking method of multiple description grid vector quantization and is transmitted in a signal channel, so as to enhance the transmission reliability of the system. Because this method is based on grid quantization technology, the complexity and encoding time delay are much lower.

The invention relates to an embedded, Chinese-English mixed language supporting, embedded application oriented voice recognition method and system for non-specific people. The invention adopts acoustical model trained by mass voice data, acoustical modeling unit set compatible with Chinese and English pronouncing mode, so as to implement Chinese-English mixed voice recognition for non-specific people. According to the invention, a plurality of background models are adopted, a Gauss mixed model (GMM) parameter is obtained by an average adaptive training executed by the background models, then a vector quantization to the difference between the average of the Gauss mixed model and the average of the background models, and the model parameters are compressed. In the recognition stage, rapid Gauss selection, acoustic score pre-calculation, and a simplified GMM model are used, so that the amount of recognition calculation and storage space of the models are greatly reduced, and the voice recognition method and system is applicable on various kinds of embedded application systems.

The LSF quantizer for a wideband speech coder comprises a subtracter for receiving an input LSF coefficient vector and removing a DC component from it; a memory-based vector quantizer and a memoryless vector quantizer for respectively receiving the DC-component-removed LSF coefficient vector and independently quantizing the same; a switch for receiving quantized vectors respectively quantized by the memory-based vector quantizer and the memoryless vector quantizer, selecting a quantized vector that has less quantized error that is a difference between the received quantized vector and the input LSF coefficent vector from among the received quantized vectors, and outputting the same; and an adder for adding the quantized vector selected by the switch to the DC component of the LSF coefficient vector.

The present invention provides a new methodology for reusing codebooks for a multistage vector quantization of parameter quantizers of signals. Prior art multistage vector quantization is done in such a way that each stage has different optimized codebooks. The prior art codebooks, thus, use quite a lot of a memory storage space. Using the same codebook stages several times, according to the present invention, reduces the memory usage and a codebook structure maintains good quality by using optimized codebooks for the most important (first) stages in the quantization. The number of codebooks is reduced by reusing the same codebooks in the refining stages. Additionally, according to the present invention, using many predictors is space-wise efficient since they need only a few of coefficients instead of larger codebooks.

The present invention relates to a method and system for compressing a continuous data flow in real-time based on lossy compression. In real-time data compression, a series of multi-dimensional data subsets acquired in a given period of time are treated as a regional data cube for the purpose of dividing a continuous series of data subsets into a plurality of data cubes. In a first embodiment implementation of parallel processing using a plurality of compression engines is facilitated by separating a data cube into a plurality of clusters comprising similar spectral vectors. By separating the data cube into clusters of similar spectral vectors no artificial spatial boundaries are introduced substantially improving image quality. Furthermore, the spectral vectors within a cluster are more easily compressed due to their similarity. In a second embodiment a predetermined number of 2D focal plane frames in a boundary area of a previous regional data cube close to a current regional data cube are included in a training set used for codevector training for the current region. Therefore, no artificial boundary occurs between the two adjacent regions when codevectors trained in this way are used for codebook generation and encoding of the spectral vectors of the current regional data cube substantially reducing image artifacts between adjacent regions. A remedy for the single bit error problem is provided in a third embodiment. Full redundancy of compressed data for a regional data cube is obtained by combining the previous regional data cube and the current regional data cube for codebook training. In order to obtain redundancy for the index map, the codebook is used to encode the current regional data cube as well as the previous regional data cube producing a baseline index map for the current regional data cube and a redundant index map for the previous regional data cube. Therefore, full redundancy for a regional data cube is provided allowing restoration of a regional data cube if its codebook and / or index map are corrupted or lost due to single bit errors.

The invention discloses a rapid hyperspectral signal compression scheme for a huge data volume of a hyperspectral image. By a compression coding scheme, the spatial and inter-spectrum correlation of the hyperspectral image is eliminated by adopting a vector quantization clustering technology, superior codeword is generated in combination with a conventional Linde Buzo Gray (LBG) clustering algorithm and a fast codeword searching algorithm, and simultaneously, coding is finished. Hadamard transform is performed on input vectors; and in a Hadamard domain, an initial codebook is constructed, andsimultaneously, fast clustering is performed by utilizing a three-step elimination inequality to effectively generate a final codebook and codes. By the hyperspectral image compression scheme provided by the invention, relatively lower image quality loss can be turned into a relatively greater compression ratio to fulfill the aim of fast coding in combination with the properties of the Hadamard transform and the characteristics of vector quantization.

The invention comprises a method for lossy data compression, akin to vector quantization, in which there is no explicit codebook and no search, i.e. the codebook memory and associated search computation are eliminated. Some memory and computation are still required, but these are dramatically reduced, compared to systems that do not exploit this method. For this reason, both the memory and computation requirements of the method are exponentially smaller than comparable methods that do not exploit the invention. Because there is no explicit codebook to be stored or searched, no such codebook need be generated either. This makes the method well suited to adaptive coding schemes, where the compression system adapts to the statistics of the data presented for processing: both the complexity of the algorithm executed for adaptation, and the amount of data transmitted to synchronize the sender and receiver, are exponentially smaller than comparable existing methods.

The invention provides a line spectrum-to-parameter dimensional reduction quantizing method based on a conditional Gaussian mixture model. Specifically, the method comprises the following steps of: firstly framing sampled voice signals, extracting LSP (Linear Spectrum Pair) characteristic parameters of the voice signals to carry out characteristic parameter dimension reduction; then dividing a characteristic parameter sequence to obtain subvector; combining a subvector parameter sequence in pairs, and establishing a union sequence; training a conditional Gaussian mixture model by utilizing the union sequence to obtain the parameters of the conditional Gaussian mixture model; calculating the conditional probability density by utilizing parameters of mean value vector, covariance matrix and the like of the conditional Gaussian mixture model, wherein the number is equal to that of a Gaussian component; then grouping the data, and including the current frame data into a group distributed by the Gaussian component with maximum conditional probability density; training a code book to the grouped data by using an LBG (Linde, Buzo and Gray) algorithm, thus finally obtaining the code book, namely the vector quantizing result of the voice signal. The line spectrum-to-parameter dimensional reduction quantizing method based on the conditional Gaussian mixture model can be used for promoting the quantizing property, and is simple to train, and low in calculation complexity.

The system carries out conversion of digital video signals organized in blocks of pixels from a first format to a second format. The second format is a format compressed via vector quantization. The vector quantization is performed by means of repeated application of a scalar quantizer to the pixels of said blocks with a quantization step (Q) determined in an adaptive way according to the characteristics of sharpness and / or brightness of the pixels and representing said vector quantization in a n-dimensional space indicative of the characteristics on n of said pixels in the block partitioned into cells of size proportional to said quantization step, each cell being assigned to an appropriate binary code, wherein said process further includes identifying at least one symmetry element in said n-dimensional space suitable for separating at least two symmetrical set of cells, and selecting one of said at least two symmetrical set of cells for the assignment of said binary codes. A symmetrical permutation on the n pixels of the block is performed according the selection and a part of said binary code indicating the status of said symmetrical permutation is conveniently set.

A digital method and system allowing crystal identification in radiation detector machines is described. The crystal identification is based on recognition of radiation detector signal shape through discrimination of detector signal's dynamic characteristics. The digital method is based on recursive and non-recursive algorithms, such as adaptive filtering combined or not with quantization methods. These digital algorithms, commonly used in other engineering applications, were modified and tailored for radiation detection. Although the method was specially designed for crystal identification measurement, which is exemplified here, it can effectively recognize the detector signal shape in any radiation detection context.

Provided is a method for converting a dimension of a vector. The vector dimension conversion method for vector quantization includes the steps of: extracting a specific parameter having a pitch period from an input speech signal and then generating a vector of a dimension that varies according to the pitch period; dividing an entire frequency domain of the generated vector of the variable dimension into at least two frequency domains; and converting the vector of the variable dimension into vectors of mutually different fixed dimensions according to the divided frequency domains. Thereby, not only an error due to the vector dimension conversion is suppressed but codebook memory required for the vector quantization is effectively reduced.

Multi-description speech coding and decoding methods and systems based on linear predictive residual classified quantization are provided. Short-time prediction parameters, long-time prediction parameters and a time-domain predictive residual signal are obtained through short-time prediction analysis and long-time prediction analysis of a to-be-coded voice stream, the residual signal is divided into multiple multidimensional coding vectors, the energy envelope of each coding vector is calculated for bit allocation, and dual-cycle vector quantizing and coding is carried out on the time-domain coefficient of each coding vector; and interlaced multi-description grouped packaging is carried out on the short-time prediction parameters and the long-time prediction parameters, and multiplexed packaging is carried out on the predictive residual quantization indexes and the coded bits of the vector quantization values of quantized coded signals.

The invention discloses a method refining a parameter of a narrow band vocoder on a decoding end. Based on the coherence of an excitation parameter and a sound track parameter, all parameters are more fine reconstructed on the decoding end, thus the quantization precision is improved, and the quality of synthetic speech sound is further improved. More particularly, for encoding and decoding parameters in a narrow band low-rate speed coding based on a hybrid excitation linear prediction model, a method based on mapping and refining among different parameters is adopted. Various encoding and decoding parameters are quantized by adopting an independent vector through an original technology. According to the invention, the coherence between the excitation parameter and the sound track parameter is considered, and various inverse-quantized encoding and decoding parameters are refined by adopting a nonlinear mapping method, thus the quantization efficiency of various parameters is increased, and the quality of the synthetic speech sound is improved. According to the method, the natural degree of the synthetic speech sound can be improved.

A video codec that allows for improved performance of embedded devices using vector quantization and the high correlation between frames in a scene to reduce the overhead associated with displaying video data. Video coding includes determining scene changes in a movie, producing one codebook for each scene, and coding each frame within a scene with the same codebook. The quantized video data is then losslessly compressed and formed into a video stream. Video decoding includes decoding the codebook for a frame and supplying the codebook and frame indices for frame rendering. Alternatively, video coding also includes dividing CPU time between codebook processing, indices processing, and frame rendering to ensure smooth video output.

A method and apparatus for reducing the complexity of linear prediction analysis-by-synthesis (LPAS) speech coders. The speech coder includes a multi-tap pitch predictor having various parameters and utilizing an adaptive codebook subdivided into at least a first vector codebook and a second vector codebook. The pitch predictor removes certain redundancies in a subject speech signal and vector quantizes the pitch predictor parameters. Further included is a source excitation (fixed) codebook that indicates pulses in the subject speech signal by deriving corresponding vector values. Serial optimization of the adaptive codebook first and then the fixed codebook produces a low complexity LPAS speech coder of the present invention.

Improved techniques are disclosed for quantizing complex vectors in communication systems. For example, a method includes the following steps. At least one complex vector representative of at least one element of a communication system is obtained. A codeword that approximates the complex vector is identified. The identified codeword is a codeword, from a set of codewords, wherein a real part of a product of the codeword and a scaled version of the complex vector is about maximal over the set of codewords. The scaled version of the complex vector is the product of the complex vector and a constant from a set of constants. In one embodiment, the element of the communication system that the complex vector represents is a channel between a base station and a user terminal in the communication system.

The method samples audio signals and divides up the signals into multiple positive and negative half waves. Sounding / not sounding determination is carried out for half wave based on up / low threshold frequency. Sub code books for corresponding dimension are searched respectively for multiple half waves determined as sounding based on number of sampled points contained between two zero points i.e. vector dimension of the half wave. Vector quantization is carried out for the sub code books to form output index. Since length of sub code book corresponding to each half wave is much smaller than length of total code book, the method reduces operation quantity of searching codes in vector quantization without lowering hearing quality of decoded audio signal. The invention also discloses decoding method and devices for executing the encoding and decoding methods.

The invention discloses an audio coding method and device for optimizing frequency domain noise shaping, and belongs to the technical field of audio coding and decoding. The method comprises the following steps: calculating a voice energy entropy of an audio signal according to the audio signal subjected to low-delay improved discrete cosine transform; judging whether the current frame is a non-voice frame or not according to the voice energy entropy of the current frame of the audio signal and a preset threshold value to obtain a non-voice frame audio signal; and after the non-speech frame audio signal is subjected to first-stage split vector quantization of frequency domain noise shaping, not performing second-stage pyramid vector quantization of a frequency domain noise shaping process. By adding a small amount of operation, the frequency of calling the second-stage pyramid vector quantization by the frequency domain noise shaping module is reduced under the condition that the tone quality is almost unchanged, and the total operand of the frequency domain noise shaping module is reduced.

The invention relates to an engine fault diagnosis method and an engine fault diagnosis device based on exhaust noise vector quantitative analysis. The method and the device mainly solve the problems of complex system and high cost because various parameters are required to be combined to complete engine fault diagnosis in the prior art. The diagnosis device comprises a characteristic extractor, a codebook generator, a memory and a recognizer, wherein the characteristic extractor is respectively connected with the codebook generator and the recognizer, the codebook generator is connected with the memory and the memory is connected with the recognizer. The Mel-frequency cepstrum coefficient (MFCC) of exhaust noise signals are used as a characteristic parameter vector sequence of a signal source in a vector quantitative algorithm, the vector quantitative algorithm is used for analyzing the exhaust noise of an engine, the difference of the exhaust noise of the engine under different working conditions is recognized and therefore the faults of the engine are diagnosed. Not only can nondestructive detection be realized, but also the system cost is low and the signals can be acquired more quickly and effectively.

The invention provides a recognition method for recognizing the on-line handwritten Chinese and Japanese. According to the method, the structured dictionary representation and the vector quantization (VQ) are adopted to establish a compact MRF-based online character recognition method, and the method can be used for recognizing large-scale Chinese and Japanese character sets. Characters are decomposed into the free radicals of basic elements for constituting characters. By means of an MRF-based model, identical free radicals for forming different characters are shared, so that the storage space of a dictionary model is saved. In addition, the method simultaneously applies the VQ technique to compress a character recognizer. Therefore, the storage space of the dictionary model is further greatly compressed on the premise that the recognition rate is not lost at all.

The present invention discloses an iteration optimum design method of selecting predictive vector quantification. The method comprises the steps of dividing training data by using conventional correlation between front and back frames, performing predictive parameter calculation and codebook design by using different training data, quantizing the training data by using designed predictive parameter and codebook and adjusting the training data division according to the quantization error value, and performing optimized selection of predictive vector quantification codebook through the training data division and codebook design iteration. According to the present invention, defect of fixed division of training data is avoided for effectively improving the performance of selecting predictive vector quantification design. The method also reduces the parameter quantizing distortion and improves the signal compression quality.