106 results about "Second quantization" patented technology

A method of processing video data includes determining a quantization level for a coefficient of a current block from a plurality of quantization levels; determining an offset value based on the quantization level, wherein the offset value is a first offset value based on the quantization level being a first quantization level or a second, different offset value based on the quantization level being a second quantization level; determining a quantization parameter or an inverse-quantization parameter for the coefficient based on the determined offset value; and as part of encoding or decoding the current block, performing one of quantization or inverse-quantization for the coefficient based on the determined quantization parameter or the determined inverse-quantization parameter.

A method and an apparatus for storing data points are provided. The method comprises: receiving the plurality of data points, each data point of the plurality of data points being represented in a floating-point representation; quantizing each one of the first plurality of data points, by: executing, during a first quantization phase: converting each data point of the plurality of data points into a corresponding first data point of a plurality of first data points; executing, during a second quantization phase: applying, to each first data point of the plurality of first data points a clamping function, thereby converting each first data point of the plurality of first data points into a corresponding second data point of a plurality of second data points; and storing the plurality of second points for further calculations instead of the first plurality of data points.

Techniques and apparatus are disclosed to improve machine learning model quantization. A parameter tensor for a machine learning model is accessed, and a set of rows, in the parameter tensor, that each include one or more outlier values is identified. The parameter tensor is decomposed into a first parameter sub-tensor corresponding to the set of rows and a second parameter sub-tensor corresponding to at least one remaining row in the parameter tensor. The first parameter sub-tensor is quantized according to a first quantization scheme, and the second parameter sub-tensor is quantized according to a second quantization scheme. A quantized version of the machine learning model comprising the quantized first and second parameter sub-tensors is generated.

The invention provides a floating-point number quantization method and device, a floating-point number inverse quantization method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises: based on a compression rule, determining quantization information corresponding to an index of each floating-point number in floating-point numbers to be quantized, the quantization information corresponding to the index of each floating-point number comprising a first quantization value and / or a second quantization value, the compression rule comprises a rule that an index which indicates that quantization information with a low compression degree belongs to is greater than an index which indicates that quantization information with a high compression degree belongs to, and for each floating-point number in the floating-point numbers to be quantized, based on a first quantization value corresponding to the floating-point number, performing quantization processing on a mantissa of the floating-point number, and / or performing quantization processing on the mantissa of the floating-point number based on a second quantization value corresponding to the floating-point number. And quantizing the index of the floating-point number based on the second quantized value corresponding to the floating-point number. By adopting the method and the device, the required average error can be met during quantization.

An encoding apparatus comprises a quantizer quantizing transform coefficients in a block in an image using a first quantization matrix, and an encoder encoding the first quantization matrix, wherein, for encoding the first quantization matrix, if using a first mode, the encoder encodes information indicating that the first quantization matrix is the same as a second quantization matrix having already been encoded, if using a second mode, encodes a difference between an element of the first quantization matrix and an element of the second quantization matrix, and if using a third mode, encodes a difference between an element and another element within the first quantization matrix, wherein a code amount of information representing the first mode is used is less than that of information representing that the second mode is used.

A device for decoding a bitstream of encoded mesh data is configured to in response to determining that first transform parameters are overridden, infer a value of a second syntax element to be equal to a first value indicating that second transform parameters are present in the bitstream of encoded mesh data; in response to the value of the second syntax element being equal to the first value, determine the second transform parameters from a second syntax structure; in response to determining that the first quantization parameters are overridden, infer a value of a third syntax element to be equal to a first value indicating that second quantization parameters are present in the bitstream of encoded mesh data; and in response to the value of the third syntax element being equal to the first value, determine second quantization parameters from a third syntax structure.

An encoding apparatus comprises a quantizer quantizing transform coefficients in a block in an image using a first quantization matrix, and an encoder encoding the first quantization matrix, wherein, for encoding the first quantization matrix, if using a first mode, the encoder encodes information indicating that the first quantization matrix is the same as a second quantization matrix having already been encoded, if using a second mode, encodes a difference between an element of the first quantization matrix and an element of the second quantization matrix, and if using a third mode, encodes a difference between an element and another element within the first quantization matrix, wherein a code amount of information representing the first mode is used is less than that of information representing that the second mode is used.

An encoding apparatus comprises a quantizer quantizing transform coefficients in a block in an image using a first quantization matrix, and an encoder encoding the first quantization matrix, wherein, for encoding the first quantization matrix, if using a first mode, the encoder encodes information indicating that the first quantization matrix is the same as a second quantization matrix having already been encoded, if using a second mode, encodes a difference between an element of the first quantization matrix and an element of the second quantization matrix, and if using a third mode, encodes a difference between an element and another element within the first quantization matrix, wherein a code amount of information representing the first mode is used is less than that of information representing that the second mode is used.

An encoding apparatus comprises a quantizer quantizing transform coefficients in a block in an image using a first quantization matrix, and an encoder encoding the first quantization matrix, wherein, for encoding the first quantization matrix, if using a first mode, the encoder encodes information indicating that the first quantization matrix is the same as a second quantization matrix having already been encoded, if using a second mode, encodes a difference between an element of the first quantization matrix and an element of the second quantization matrix, and if using a third mode, encodes a difference between an element and another element within the first quantization matrix, wherein a code amount of information representing the first mode is used is less than that of information representing that the second mode is used.

The invention discloses a traffic flow prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a traffic flow sequence of a target intersection in a first historical time period; standardizing each first traffic flow data of the traffic flow sequence, and determining corresponding second traffic flow data; quantizing each second traffic flow data in a preset historical window, and determining each corresponding first quantized character; inputting each first quantized character into the large language model, and determining a second quantized character of at least one sub-time period in the prediction window based on the large language model; and finally, carrying out inverse quantization and inverse standardization on the second quantization character, and determining corresponding predicted traffic data. And traffic flow prediction based on the large language model is realized. The problems of high popularization and generalization cost and poor expandability of traffic flow prediction based on statistics, machine learning and deep learning prediction models in the prior art are avoided. And the popularization and generalization performance of the traffic flow prediction model is improved.

The invention relates to the technical field of model quantification, and discloses a model quantification method and device, equipment and a storage medium, and the method comprises the steps: carrying out the model quantification of a target model according to a quantification configuration parameter, and obtaining a first quantification model; obtaining an overall quantization loss corresponding to the first quantization model and a network quantization loss corresponding to each layer of network in the first quantization model; if the overall quantization loss is greater than a preset loss threshold, selecting a quantization loss area according to the network quantization loss; carrying out operator splitting on a network corresponding to the quantitative loss region in the target model to obtain an adjusted target model; performing model quantization on the adjusted target model based on the quantization configuration parameter to obtain a second quantization model; and if the overall quantization loss corresponding to the second quantization model is less than or equal to a preset loss threshold, taking the second quantization model as a quantization completed model. Due to the fact that the operator is split and then quantized, the quantization effect close to channel-by-channel quantization is achieved, and it is guaranteed that the overall quantization loss of the model can be reduced.

The invention discloses a language task processing method and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: selecting a target weight parameter according to task calibration data and an activation function type of an original task processing model; according to each target weight parameter, the maximum weight parameter and the task calibration data, determining a fitting processing result of the quantization parameter; and determining a first quantization error function and a second quantization error function carrying a scaling factor according to a fitting processing result, and obtaining a numerical value of the scaling factor by minimizing a ratio of the first quantization error function and the second quantization error function. And performing scaling processing on each target weight parameter by using the scaling factor, and quantifying each scaling weight parameter based on the quantization precision parameter to obtain a task processing model for executing the to-be-processed language task. According to the method and the device, the problem that high accuracy of a language task processing result cannot be ensured on the basis of reducing resources used in a language task execution process in related technologies can be solved, and the language task processing precision is effectively improved.

The present invention relates to the field of artificial intelligence technology, and discloses a quantization operation method, an electronic device, and a program product for a neural network model. The quantization operation method includes: determining a first operator and a second operator that need to perform an association operation, where the first operator has first output data and the second operator has second output data; obtaining a common quantization coefficient based on a first quantization coefficient corresponding to the first output data and a second quantization coefficient corresponding to the second output data; obtaining first quantization data corresponding to the first output data according to the first output data and the common quantization coefficient, and obtaining second quantization data corresponding to the second output data according to the second output data and the common quantization coefficient; performing an association operation on the first quantization data and the second quantization data corresponding to the first operator and the second operator to obtain a quantization operation result of the association operation of the first operator and the second operator. Based on the above solution, the model operation speed and accuracy can be effectively improved.

An encoding method, an encoding device, a decoding method, a decoding device, and a transmission system, relating to the technical field of signal processing. The encoding method comprises: performing first quantization processing on a signal to be encoded, to obtain an initial quantized signal (110); performing second quantization processing on the signal to be encoded, to obtain a first multiple-description quantized signal (120), wherein the number of first pulses used in the first quantization processing is greater than the number of second pulses used in the second quantization processing; on the basis of the initial quantized signal and the first multiple-description quantized signal, obtaining a second multiple-description quantized signal of the signal to be encoded (130); and generating a bitstream on the basis of the first multiple-description quantized signal and the second multiple-description quantized signal (140).

An encoder, includes: memory; and circuitry coupled to the memory, in which in a first encoding process, the circuitry quantizes a current block using a first quantization matrix in both of a first case where the circuitry performs primary transform and the circuitry does not perform secondary transform and a second case where the circuitry performs both the primary transform and the secondary transform, in a second encoding process, the circuitry quantizes the current block using a second quantization matrix in a third case where the circuitry performs primary transform and the circuitry does not perform secondary transform, and in a third encoding process, the circuitry quantizes the current block without using a quantization matrix in both of a fourth case where both the primary transform and the secondary transform are skipped and a fifth case where both the primary transform and the secondary transform are performed.

Embodiments of the present application disclose a method and apparatus for determining a quantization parameter offset, a device, a storage medium and a program product. The method comprises: obtaining a first quantization parameter offset and a second quantization parameter offset of a coding unit, the first quantization parameter offset being calculated based on spatial information of the coding unit, and the second quantization parameter offset being calculated based on temporal information of the coding unit; determining representation information of the first quantization parameter offset, the representation information representing whether the first quantization parameter offset is used to improve reconstruction quality of the coding unit; determining a target quantization parameter offset of the coding unit according to the representation information of the first quantization parameter offset, the first quantization parameter offset and the second quantization parameter offset; and encoding the coding unit based on the target quantization parameter offset. The embodiments of the present application can effectively improve coding efficiency.

The invention relates to intra-tree geometric quantization of point clouds. An example apparatus includes one or more processors and a memory storing instructions that, when executed by the processors, cause the processors to receive points representing a point cloud in a three-dimensional space and generate a data structure representing the point cloud. Generating the data structure includes: encoding a location of each point in each dimension as a bit sequence according to a tree data structure; dividing each of the sequences into two or more portions according to a scaling depth; determining that a subset of the points is spatially isolated from the remainder of the points; quantizing each of the portions associated with the subset of the points according to a first quantization step size; quantizing each of the portions associated with the remaining portion of the points according to a second quantization step size; and including the quantized portion in the data structure.

The invention discloses a decoding method, a decoding device, decoding equipment and a medium. The method comprises the following steps: acquiring a current log-likelihood ratio corresponding to a to-be-decoded signal and a corresponding historical log-likelihood ratio; comparing the reliability difference between the current log-likelihood ratio and the historical log-likelihood ratio, and determining a corresponding first quantization factor and a corresponding second quantization factor for the current log-likelihood ratio and the historical log-likelihood ratio according to the reliability difference; and quantizing the current log-likelihood ratio by using the first quantization factor, quantizing the historical log-likelihood ratio by using the second quantization factor, and performing merging and decoding processing based on the two quantized log-likelihood ratios. According to the method provided by the invention, the adverse effect of the log-likelihood ratio with relatively low reliability on the decoding performance can be avoided, and the improvement effect of the log-likelihood ratio with relatively high reliability on the decoding performance is improved.

The invention provides a quantization parameter storage method and device, a model reasoning method and device and a storage medium, and relates to the technical field of computers, in particular to the technical field of large models, the technical field of artificial intelligence and the technical field of model quantization. According to the specific implementation scheme, a calculation unit of a processor performs statistics based on reference data to obtain a statistical value of a first quantization parameter of a model; searching a target value of the first quantization parameter and a target value of a second quantization parameter of a model in a search space through the calculation unit based on the statistical value of the first quantization parameter; a target value of the first quantization parameter and a target value of the second quantization parameter are stored in a memory by the calculation unit. The quantization parameters are calculated and stored in advance, so that the quantization parameters which are calculated in advance can be read according to specific use requirements for quantization reasoning, occupation of a memory of a processor is reduced, calculation resources needing to be occupied in the reasoning process are reduced, and the reasoning speed and efficiency are improved.

The present invention provides a data quantization method, device, and storage medium, relating to the field of wireless communication technology. The method includes: performing digital down-conversion processing on an input signal based on a first quantization bit width to obtain a baseband signal; the first quantization bit width is determined based on the peak-to-average ratio of the input signal; performing channel estimation and equalization processing on the baseband signal based on a second quantization bit width; the second quantization bit width is obtained based on real-time calculation of the power of pilot symbols in the baseband signal; demodulating the baseband signal after channel estimation and equalization processing, and dynamically truncating the demodulated soft information based on the second quantization bit width and modulation mode to obtain truncated soft information; performing saturation quantization truncation processing on the truncated soft information, and outputting the truncated soft information to a decoder. This optimizes signal quantization accuracy and resource efficiency.

The invention discloses a model quantification method and device, equipment, a storage medium and a computer program product, and relates to the field of model quantification, and the method comprises the steps: carrying out the training and quantification of a full-precision model disposed on edge equipment, and obtaining a first quantification model which comprises a first layer set; according to a sensitivity index and training cost corresponding to each model layer in the first layer set, a target training layer is selected from the first layer set, and the sensitivity index is used for representing the influence degree of the quantization operation on the model layer; performing quantitative perception training on the target training layer in the first quantitative model to obtain a second quantitative model, the second quantitative model including a second layer set; and performing bit width allocation on each model layer in the second layer set to obtain a target quantitative model. According to the invention, while the precision of the quantitative model is ensured, the cost required by quantitative training is reduced, so that the contradiction between insufficient quantitative precision after training and too high quantitative perception training cost in the prior art is solved.

The application discloses an AVS3 video encoding method, device and equipment and a computer storage medium, relates to the technical field of video processing, and obtains a target video to be encoded; a basic quantization parameter determined according to a set encoding mode is obtained; a first quantization scaling, a first quantization offset, a first quantization boundary and a first quantization step of the set encoding mode are obtained; a second quantization step of the AVS3 video encoding is obtained; conversion information between the second quantization step and the first quantization step is generated; the first quantization scaling, the first quantization offset and the first quantization boundary are respectively converted according to the conversion information, and a second quantization scaling, a second quantization offset and a second quantization boundary are obtained; the basic quantization parameter is adjusted based on the second quantization scaling, the second quantization offset and the second quantization boundary, and a target quantization parameter is obtained; and the target video is subjected to AVS3 video encoding based on the target quantization parameter, and an encoding result is obtained. The visual quality of the AVS3 video encoding is improved.

A device for decoding a bitstream of encoded mesh data is configured to in response to determining that first transform parameters are overridden, infer a value of a second syntax element to be equal to a first value indicating that second transform parameters are present in the bitstream of encoded mesh data; in response to the value of the second syntax element being equal to the first value, determine the second transform parameters from a second syntax structure; in response to determining that the first quantization parameters are overridden, infer a value of a third syntax element to be equal to a first value indicating that second quantization parameters are present in the bitstream of encoded mesh data; and in response to the value of the third syntax element being equal to the first value, determine second quantization parameters from a third syntax structure.

The invention provides a model quantification method and device, computer equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: for any network layer of an image classification model, under the condition that the weight of the network layer and an input feature value of an input image in the network layer are distributed in a long tail manner, respectively quantizing the weight and the input feature value based on a first quantization bit width to obtain a first quantization weight and a first quantization feature value of the network layer; under the condition that the first quantization bit width is not smaller than the second quantization bit width, the first quantization weight and the first quantization feature value are cut off, and a second quantization weight and a second quantization feature value with the length being the second quantization bit width are obtained; and weighting the second quantization weight and the second quantization feature value to obtain an output feature value of the network layer. According to the scheme, the calculation overhead of the multiplier can be effectively reduced under the condition that the large numerical value representation range and the high quantization precision of the second quantization weight and the second quantization feature value are kept.

The invention provides a video processing method, receiving end equipment and transmitting end equipment, and relates to the technical field of video transmission. The video processing method comprises the following steps: in response to a condition that a first health degree score of a network at a current moment is less than or equal to a first threshold value, discarding at least part of video coding frames in a buffer area, and sending a request for obtaining an instant decoding refreshing frame to a sending end; in response to receiving an instant decoding refresh frame, decoding the instant decoding refresh frame, the instant decoding refresh frame being obtained by encoding a target region in the original image frame based on a first quantization parameter and encoding a background region in the original image frame based on a second quantization parameter, the first quantization parameter does not change along with the change of the first health degree score, and the second quantization parameter is negatively correlated with the first health degree score. According to the method and the device, the delay of the video picture can be reduced, and the information of the target area can be clearly reserved, so that the stability, accuracy and safety of remote operation can be improved.

Various embodiments provide an encoder that performs an up-conversion and a down-conversion on a first quantization matrix to generate a second quantization matrix, and quantizes transform coefficients of a current block using the second quantization matrix. The first quantization matrix has a first number of rows and a first number of columns equal to the first number of rows, and the second quantization matrix has a second number of rows and a second number of columns different from the second number of rows. In the up-conversion, the circuitry generates the second quantization matrix such that one of the second number of rows or the second number of columns is larger than the first number of rows. In the down-conversion, the circuitry generates the second quantization matrix such that the other of the second number of rows or the second number of columns is smaller than the first number of rows.

To solve the problem in which a quantization error is accumulated in each layer of a neural network and is finally increased to be output.SOLUTION: An information processing apparatus comprises: acquisition means that acquires second quantization information which includes information associated with quantization of a second network in the information processing apparatus that generates a deep layer learning model having a first network and the second network connected to the first network; determination means that generates first quantization information which is information associated with quantization of the first network on the basis of the second quantization information; and first quantization means in which first quantization based on the first quantization information is applied to the first network.SELECTED DRAWING: Figure 3

The invention provides a sheet material defect size quantification and motion condition detection method in rapid motion, which comprises the following steps: acquiring two-axis motion-generated eddy current signals of defects of experimental sheet materials with different motion speeds, lift-off distances and defect sizes, the two-axis motion-generated eddy current signals comprising parallel and vertical motion-generated eddy current signals; multiple characteristic parameters are obtained, and then a sample data set is constructed; a quantitative detection model is constructed based on a neural network, a parallel feature parameter input module in the quantitative detection model is connected with a fusion module through a first quantitative detection module, and a vertical feature parameter input module is connected with the fusion module through a second quantitative detection module. The output of each of the two quantitative detection modules comprises a movement speed, a lift-off distance and a defect size; training the model to obtain a target quantitative detection model; and obtaining a target two-axis motion-generated eddy current signal of the defect of the target thin plate material to be quantified, and outputting the defect size, the motion speed and the lift-off distance of the target thin plate material by using the target quantification detection model.

The present invention relates to the technical field of hybrid quantization, and in particular to a hybrid quantization method, device and electronic device for a large visual language model. The method comprises: inputting a constructed image encoder calibration set into a pre-established image encoder and a text encoder, selecting a quantization scheme from a pre-defined search space of hybrid precision quantization to quantize the image encoder after training, and calculating a first quantization error based on the outputs of the image encoder and the text encoder before and after quantization; inputting a multimodal calibration set into a large visual language model, and selecting a quantization scheme from the search space to perform hybrid precision quantization on the large visual language model, and calculating a second quantization error based on the outputs of the large visual language model before and after quantization; storing each quantization scheme and the corresponding quantization error in a hash table; and using a policy gradient-based reinforcement learning algorithm in combination with the hash table to optimize the strategy for generating a quantization scheme. The hash table is used to accelerate the search process of the quantization scheme and improve the optimization efficiency.

The application discloses a multi-array geomagnetic underground pipeline detection system and method. The system comprises one or more geomagnetic sensor arrays, each array comprising a group of three-axis fluxgate sensors, an analog-to-digital converter (ADC), a geomagnetic background field cancellation signal generation circuit and an analog differential summation node. For a first time sequence segment, the ADC generates first digital geomagnetic time sequence data with a first quantization bit width; the cancellation signal generation circuit generates an analog cancellation signal according to prior background field data, and cancels a known strong magnetic interference component from subsequent analog signals at the analog differential summation node; then the ADC is dynamically adjusted to a second quantization bit width smaller than the first quantization bit width; and the equivalent magnetic moment vector data of a weak magnetic target body is determined by calculating a scaling factor corresponding to a least square error in combination with an amplitude reference benchmark in the first digital geomagnetic time sequence data.