196 results about "Weight prediction" patented technology

Provided are image encoding and decoding methods and apparatuses using weighted bi-prediction. The image decoding method and apparatus according to one embodiment obtain one or more prefixes including a first counter value indicating the number of additional syntax elements subsequent to main syntax elements from a received bitstream, obtain the additional syntax elements including weight information and information about whether to perform weighted bi-prediction on prediction units separated for predicting a current block, based on the one or more prefixes, and generate a prediction block including a prediction value, based on the main syntax elements and the additional syntax elements.

A method and apparatus effectively encode multiple layers using interlayer information in a multi-layered video codec. The method includes reading information concerning the low layer picture, calculating weighting factors using the information concerning the low layer picture, calculating a weighted sum of reference pictures for the current picture using the weighting factors and generating a predicted picture for the current picture, and encoding a difference between the current picture and the predicted picture.

The invention relates to a cow shape measuring method and a cow shape measuring system based on a stereo vision technology. The method comprises the following steps: collecting image information of cow samples at different angles, and calculating body size data of the cow samples according to the image information at different angles; acquiring weight data of the cow samples, and building a weight prediction model according to the corresponding relationship between body size data and weight data; calculating body size data of a cow to be measured according to the image information of the cow to be measured at different angles, and inputting the body size data of the cow to be measured into the weight prediction model to estimate the weight data of the cow to be measured. By using the technical scheme provided by the embodiment of the invention, the problem that weight data measured by a weighbridge is inaccurate due to dynamic nature of cows is solved; and the body size data of cows is acquired through image acquisition, so that the problem that an artificial field contact method is time-consuming and labor-consuming, is of strong subjectivity, and causes stress reaction of cows is solved, and full-automatic cow body size data measurement and weight data measurement is realized.

The invention relates to a fish body dimension and weight rapid acquisition apparatus. The apparatus comprises a window-shaped fixing device and a dimension reference object. The upper inner wall of the window-shaped fixing device is provided with a high-angle shooting pick-up head. One side wall of the window-shaped fixing device is provided with a lateral-shooting pick-up head. One side wall opposed to the side wall where the lateral-shooting pick-up head is located is provided with an LED lateral-shooting lamp box. The lower inner wall of the window-shaped fixing device is provided with an LED high-angle shooting lamp box. The bottom of the window-shaped fixing device is further provided with an electronic balance. The acquisition method of the acquisition apparatus comprises the following steps of shooting a background image respectively in a high-angle shooting way and a lateral shooting way when no fish is present; shooting a dimension reference object image respectively in a high-angle shooting way and a lateral shooting way, and setting up a model for fish body dimension calculation and fish weight prediction. The fish body dimension and weight rapid acquisition apparatus which can rapidly acquire multi-parameter fish body dimension indexes and weights of fish bodies can further set up a fish body dimension and weight prediction model in a computer. The prediction precision can reach more than 90%. The model can be applied to research and development of fish weight sensors.

The invention provides a coding and decoding method and apparatus, and the method comprises the steps: obtaining a weighted prediction value of a current block through the following steps when it is determined that intra-frame and inter-frame weighted prediction is started, and enabling the weighted prediction value to be used for the coding or decoding of the current block; obtaining a first prediction mode of a first adjacent block of the current block and a second prediction mode of a second adjacent block of the current block; determining a target prediction mode of intra-frame predictionaccording to the first prediction mode and the second prediction mode, and obtaining an intra-frame prediction value of the current block according to the target prediction mode; obtaining a motion information candidate list of the current block, wherein the motion information candidate list comprises at least one piece of motion information; determining target motion information of inter-frame prediction according to the motion information candidate list, and obtaining an inter-frame prediction value of the current block according to the target motion information; and carrying out weighting processing on the intra-frame prediction value and the inter-frame prediction value to obtain the weighted prediction value. Through the technical scheme of the invention, the prediction performance can be improved.

The invention relates to a grain storage quantity detection method for horizontal warehouses and shallow silos. The grain storage quantity detection method comprises the following steps of: firstly selecting specific pressure sensors to lay a grain warehouse pressure sensor detection network; distributing the pressure sensors along the side wall of the grain warehouse in the mode that all the pressure sensors are evenly-spaced laid at d meter distance from the side wall; and determining whether a grain warehouse is calibrated or not so as to obtain a calibration parameter and a grain warehouse weight prediction model, spreading the top surface of a grain pile in the grain warehouse after the grain warehouse is fully filled with the grain, detecting the average output valve of the pressure sensor at the bottom of the grain warehouse, and detecting the grain storage quantity of the grain warehouse according to an actually-tested bottom surface area of the grain warehouse, a bottom surface area of the calibrated grain warehouse and calibrated parameters by using a model so as to obtain an estimated value of the weight of the grain warehouse. According to the grain storage quantity detection method provided by the invention, the pressure sensors are required to be arranged on the bottom surface of the grain warehouse, and a compensating item of a side grain warehouse friction action is added into a bottom surface pressure estimating model in order to avoid using the side pressure sensor and accurately obtain the estimated value of the pressure of the bottom surface of the grain warehouse, so that the quantity of the sensors is obviously reduced, and the device detection cost and the installation and maintenance cost are reduced.

The present invention relates to an image processing apparatus and an image processing method capable of performing weighted prediction on the basis of local characteristics of an image.An inter-TP motion prediction / compensation unit 76 performs a matching process on a block of an image of a frame to be encoded using an inter-template matching method and performs implicit weighted prediction using a weighting coefficient computed from the pixel values of a template region for the matching. The weighting coefficient is computed by a weighting coefficient computing unit 77. The present invention is applicable to, for example, an image encoding apparatus that performs encoding using the H.264 / AVC standard.

A decoder (10) conceals errors in a coded image comprised of a stream of macroblocks by examining each macroblock for pixel errors. If such errors exist, then each of at least two macroblocks pictures from each of two different pictures are weighted to yield a weighted prediction (WP) for estimating missing / corrupt values to conceal the macroblock found to have pixel errors.

The invention relates to a text message based waveform concatenation speech synthesis method. The text message based waveform concatenation speech synthesis method includes steps of S1, extracting acoustic parameters and text parameters of all elements in an original voice frequency through segment cutting, and training a duration prediction model and a weight prediction model according to extracted parameters; S2, using a layered pre-selection method to primarily pre-select the elements in a corpus to obtain candidate elements by means of a target element of text analysis and a duration predicted by the duration prediction model; S3, calculating the target element, the candidate elements, and weight information predicted by the weight prediction model to obtain a target cost; calculating Integrating degrees of two adjacent elements to obtain a concatenation cost; using a viterbi searching method to search the target cost and the concatenation cost to obtain a minimum cost path so as to further obtain an optimum element and obtain synthesis speeches through smooth concatenation.

The invention belongs to the field of video coding and decoding technology, and discloses a method for making an inter-frame weighted prediction based on a reference quality. The method comprises thefollowing steps: for a coding mode that requires motion estimation, first calculating a weight of a corresponding reference list based on a reference frame quality to obtain a first weight mode; thenselecting a preferred weight mode from the first weight mode and a default weight mode in combination with a rate distortion cost, searching an optimal matching block for a current coded block, and performing a weighted prediction in the preferred weight mode; and for a coding mode that does not require motion estimation, selecting an optimal weight from the weights of coded blocks at all candidate list positions of the current coded block based on the rate distortion cost, and then performing a weighted prediction based on the optimal weight. The method not only can improve the accuracy of searching the matching block, but also can reflect a different matching degree to the current coded block when a motion compensation block performs weighting. The calculation of weights is adaptive, which reduces the complexity.

The invention relates to an undirected network edge-connectivity weight prediction method based on node similarity. The undirected network edge-connectivity weight prediction method comprises following steps of 1) establishing an undirected network diagram in dependence on an existing data set including nodes and the edge-connectivity weight; 2) respectively calculating following three kinds of similarity indexes including the local similarity index, the global similarity index and the semi-local similarity index in the network diagram in step 1); and 3) predicting the edge-connectivity weight in the test set in dependence on the three kinds of similarity indexes calculated in the step 2) and by means of a multivariate linear regression model, and then examining the model mean performances including the Pearson coefficient and the root-mean-square value by means of a tenfold crossing verification method. According to the invention, by means of the node similarity, the missing edge-connectivity weight is predicted through the multivariate linear regression model, the model is simple, and the prediction result is good.

Systems and methods for determining body weight predictions and human conditions are disclosed. A body weight may be predicted by capturing at least one image of a human, and determining, from the image, a body weight prediction of the human by processing the at least one image with a data processor. The body weight prediction may further be based on an age-based weight factor. A model such as a neural network model may be used to predict body weight.

An image coding apparatus which compression-codes moving picture data including a current image to be coded, the image coding apparatus including an accumulating unit which accumulates a reference image, a predicted image generating unit which generates a predicted image of the current image to be coded, based on the reference image accumulated in the accumulating unit, by performing motion compensation with weighted prediction or motion compensation without the weighted prediction, the weighted prediction being a process of adding a predetermined offset value to a pixel value of the reference image, a coding unit which codes a difference between the predicted image generated by the predicted image generating unit and the current image to be coded, and a determining unit which determines whether or not the current image to be coded is included in, among the moving picture data, a picture group which is a group of successive pictures and indicates motion equal to or larger than a predetermined amount of motion, in which the predicted image generating unit generates the predicted image by performing the motion compensation without the weighted prediction when the determining unit determines that the current image to be coded is included in the picture group, and to generate the predicted image by performing the motion compensation with the weighted prediction when it is determined that the current image to be coded is not included in the picture group.

The present invention discloses an apparatus and method for providing a motion estimation method with weight prediction that requires less memory and computation cycles. In one embodiment, the weight is applied to pixels of a current slice or picture instead of the reference picture. In doing so, the number of processing cycles is significantly reduced while retaining the benefits of implementing a motion estimation method with weight prediction.

The invention discloses a speech dereverberation method based on depth characteristics of a generative adversarial network, the method mainly comprises the following steps of: carrying out signal preprocessing on speech through weighted prediction error (WPE); carrying out characteristic extraction of data: carrying out MFCC characteristic extraction on speech data and bottleneck characteristic BNF extraction aiming at phonemes without reverberation speech; then carrying out signal preprocessing on speech through WPE: constructing the generative adversarial network with depth characteristics containing the MFCC characteristics of reverberation speech mapped to clean speech through the generative adversarial network; finally carrying out forced alignment by using a traditional GMM-HMM through a Kaldi toolbox, and then carrying out acoustic model training and decoding by using a depth neural network. The speech dereverberation method combines a method of signal processing with a deep learning framework based on the generative adversarial network to enable a system to combine the respective advantages of both to produce a better speech dereverberation effect.

Embodiments of the invention provide a method and a device for measuring the weight of a live pig, and relate to the technical field of artificial intelligence. The method comprises the following steps: acquiring a video image about the to-be-measured live pig; inputting the video image into a pre-trained key point detection model to obtain a key point thermodynamic diagram and key point positioninformation of the to-be-measured live pig output by the model; obtaining a plurality of preset key points according to the key point position information; calculating the hip width, the hip height and the body length of the to-be-measured live pig according to the preset key points of the to-be-measured live pig; calculating the size of the to-be-measured live pig according to the hip width, thehip height and the body length; and inputting the hip width, the hip height, the body length and the size into a preset live pig weight regression model for calculation to obtain a weight prediction value of the to-be-measured live pig. According to the technical scheme, the problem of low accuracy of visually measuring the weight of the live pig in the prior art can be solved.

A video processing method includes generating, for a conversion between a current block of a video and a bitstream representation of the video, a motion vector for the current block; invoking a weighted prediction processing tool or a second processing tool in a mutually exclusive manner; and performing the conversion according to the invoking. The invoking of the second processing tool comprises refining the motion vector.

The invention provides a least square support vector machine (LSSVM) periodic weighting prediction method based on wavelet and chaos optimization. The method utilizes the wavelet decomposition technology to enable selected sample set data to be decomposed into components of different frequencies. A component phase space is reconstructed on the basis of the chaos theory, the reconstructed components are trained by using LSSVM models, parameters of LSSVM prediction models are optimized through chaos particle swarm optimization, and finally wavelet reconstruction is performed on prediction components obtained by the LSSVM models to whole periodic weighting load prediction waveforms. Through wavelet decomposition of a periodic load, reconstruction of a load phase space on the basis of the chaos theory, construction of a least square support vector machine, and chaos particle swarm optimization of the parameters, the LSSVM periodic weighting prediction method based on the wavelet and the chaos optimization can predict periodic weighting load waves at some period of reconstructed waves when load time sequence has certain chaos characteristics and can be widely used for prediction of periodic weighting load to be born of a support.

The invention discloses a non-runoff small hydropower station short term power weighted prediction method which comprises a step of collecting the data information of a regional non-runoff small hydropower station, a step of calculating the first short term power prediction value of an ith non-runoff small hydropower station at a jth time, a step of calculating the first short term power prediction value of the regional non-runoff small hydropower station at the jth time, a step of obtaining the second short term power prediction value of the regional non-runoff small hydropower station at the jth time by using a support vector machine according to the hitorical data of the ith non-runoff small hydropower station, meteorological data and peak and valley prices of a regional power grid, a step of determining a weight coefficient, and a step of calculating the short term power prediction value Pnrj of the regional non-runoff small hydropower station at the jth time of a prediction day. The technical problem that a direct prediction method is mainly used by the non-runoff small hydropower station short term power prediction in the prior art, the prediction method depends on the subjective experience, knowledge and other comprehensive judgment ability of an operator, and a prediction error is large is solved.

Provided is a lexical item weight query learning method based on a recurrent neural network, comprising the steps of: S1, searching for an optimal lexical item weight; S2, constructing a query lexical item characteristic vector; S3, constructing a query lexical item weight learning model; and S4, utilizing the query lexical item weight learning model to predict a query lexical item weight. The invention creatively provides the lexical item weight query learning method which can convert a query lexical item weight prediction problem into a sequence labeling problem, and automatically and efficiently predict a query lexical item weight; the increased ranges of a main evaluation index MAP for a data set are respectively 16.8% (Robust04) and 11.8% (GOV2), which prove the effectiveness of the lexical item weight query learning method for a query lexical item weight learning task.

The invention relates to a power grid security risk prediction method. The method comprises steps that screened network security posture factors are processed; security posture state classification standards are determined; a hidden Markov model is constructed according to actual power grid conditions; hidden Markov model parameters are updated according to present security posture of equipment; security posture of an integral electric power network is calculated; a prediction risk value of the equipment and a weight prediction risk value are calculated. Through the method, the network security posture factors are deeply researched, the factors having greatest influence on the security posture are screened out, data processing workload is reduced, model training is carried out through utilizing the data relevant to the security posture factors, a problem of over-hugeness of a hidden Markov transition matrix is effectively alleviated, moreover, problems of non-high prediction precision and parameter determination difficulty are alleviated.

Disclosed are an image processing device, an image processing method, and a program, with which it is possible to inhibit the loss of high-frequency components and provide clear images. The disclosed selector (95) selects a filter coefficient from among the filter coefficient (A1) which is stored in the A1 filter coefficient memory (91) and used for all inter prediction modes when L0 / L1 weighted prediction is not employed, the filter coefficient (A2) which is stored in the A2 filter coefficient memory (92) and used for the bi-predictive mode when employing L0 / L1 weighted prediction, the filter coefficient (A3) which is stored in the A3 filter coefficient memory (93) and used for the direct mode when employing L0 / L1 weighted prediction, and the filter coefficient (A4) which is stored in the A4 filter coefficient memory (94),and used for the skip mode when employing L0 / L1 weighted prediction, and the selector outputs the selected filter coefficient to the fixed interpolation filter. The disclosed device and method can be applied to an image encoding device which performs encoding according to H.264 / AVC, for example.

The invention discloses a text information-based waveform concatenation voice synthesizing method. The method comprises the following steps: extracting acoustic parameters and text parameters of all primitives from original audio through sound segment segmentation, and training a time length prediction model and a weight prediction model according to the extracted parameters; performing layered pre-selection by adopting a layered pre-selecting method; performing primary pre-selection on the primitives in a language database by utilizing a target primitive analyzed from a text and the time length predicted by the time length prediction model to obtain candidate primitives; calculating the target primitive, the candidate primitives and the weight information predicted by the weight prediction model to obtain target cost; calculating the integrating degree between two adjacent primitives to obtain concatenation cost; searching the target cost and the concatenation cost by using a Viterbi search method to obtain the least cost path, so as to obtain an optimal primitive and a synthesized voice through smooth concatenation. According to the method, the voice synthesizing efficiency is improved; the real-time performance of the concatenation synthesis is enhanced; the prosodic features of the synthesized voice are improved.

The invention discloses a material demand weighted prediction method based on enterprise MRP. The method comprises the following steps: firstly, determining a minimum material purchasing period T anda material purchasing advance period LT, and obtaining multi-dimensional data such as daily inventory, ex-warehouse and MRP; secondly, based on historical daily real ex-warehouse data, calculating byusing a moving average method to obtain an average value Davg of purchasing advance demand DLT; calculating the ratio distribution of the historical demand average value Davg to the historical daily MRP data, and respectively obtaining a maximum value rmax and a minimum value rmin; calculating an actual weight Wave and an MRP weight Wmrp according to the ratio rt of the historical demand average value Davg to the MRP of the current test day; and finally, the model estimates a purchase advance demand DLT and a minimum purchase cycle demand DT based on the weight coefficient to complete demand prediction. According to the method, the weight coefficients of the MRP data and the historical warehouse-out rule are designed for demand prediction, so that the defects of the MRP are greatly improved, and a scientific reference basis is provided for enterprises to purchase materials.

The invention provides a magnetic resonance image fusion method based on a weight prediction network. The magnetic resonance image fusion method comprises the steps: firstly designing an artifact removal network, and carrying out artifact recognition and suppression on an input multi-mode image; secondly, constructing a weight prediction network, generating fusion weight prediction graphs of images with different contrast ratios, and endowing regions with rich details with higher weights; and finally, establishing a fusion network, and inputting the image subjected to the artifact suppression and the corresponding weight prediction image to obtain a composite image with the advantage of each modal contrast ratio. According to the magnetic resonance image fusion method, the magnetic resonance image is subjected to artifact preprocessing, so that the image fusion applicability is effectively improved; by integrating the advantages of the images with different contrast ratios, the biological tissue structure can be described more comprehensively and accurately. Compared with a traditional method, complex feature extraction does not need to be designed, the obvious anti-artifact capability is achieved, the fusion effect is good, and medical diagnosis and treatment guided by images are facilitated.

The invention relates to a video weighted prediction system and a coding and decoding method thereof based on the H.264 standard and the AVS standard in the technical field of image processing, comprising an arithmetic module, a control module, a parameter storage module and a lookup table, wherein, the arithmetic module is connected with the control module so as to transmit a weighted prediction parameter, the control module is respectively connected with the arithmetic module, the parameter storage module and the lookup table so as to transmit the weighted prediction parameter and a control instruction, and the arithmetic module receives a weighted prediction request and outputs a weighted prediction result. The invention uses a set of weighted prediction arithmetic circuit, and input data for forward weighted prediction and rearward weighted prediction does not need to be generated simultaneously, thereby meeting the requirement on speed, saving hardware resources and efficiently accomplishing the H.264 standard and the AVS standard weighted prediction.

Techniques are described for video encoding and decoding with same picture or count numbering for scalability support. One example involves obtaining a portion of a picture (e.g., a slice, a block, or other portion) and determining if weighted prediction is enabled for the portion of the picture. When weighted prediction is enabled, a zero value picture order count offset indicating a reference picture from a reference picture can be used, though different portion of the picture (e.g., different slices of a picture) can have different picture order count offset values. The portion of the picture can then be reconstructed using the reference picture identified by the zero value picture order count offset. Additional embodiments can use weighted prediction flags and different offset value determinations to support scalability or reference pictures in weighted prediction with a different size than the picture being reconstructed.

The invention provides a coding and decoding method and coding and decoding equipment, and the method comprises: when the intra-frame and inter-frame weighted prediction is determined to be started, obtaining a weighted prediction value of a current block through the following steps, wherein the weighted prediction value is used for the coding or decoding of the current block; obtaining a first prediction mode of a first adjacent block of the current block and a second prediction mode of a second adjacent block of the current block; determining a target prediction mode of intra-frame prediction according to the first prediction mode and the second prediction mode, and obtaining an intra-frame prediction value of the current block according to the target prediction mode; obtaining a motioninformation candidate list of the current block, wherein the motion information candidate list comprises at least one piece of motion information; determining target motion information of inter-frameprediction according to the motion information candidate list, and obtaining an inter-frame prediction value of the current block according to the target motion information; and performing weighting processing on the intra-frame prediction value and the inter-frame prediction value to obtain a weighted prediction value. Through the technical scheme of the invention, the prediction performance canbe improved.

A method of decoding a video signal according to the present invention may include: determining whether or not change in brightness is present between a current video including a current block and a reference video of the current video; when it is determined that change in brightness is present between the current video and the reference video, determining weight prediction parameter candidates of the current block; determining a weight prediction parameter of the current block on the basis of index information specifying any of the weight prediction parameter candidates; and performing prediction for the current block on the basis of the weight prediction parameter.