14results about How to "Reduce coding time" patented technology

The invention discloses a VVC all-zero block skipping method based on a multi-level framework, which comprises the following steps of: reading a transformation coefficient from a transformation block TU, comparing the amplitude of the maximum transformation coefficient with a pre-calculated threshold value, if the amplitude is smaller than the threshold value, judging that a true all-zero block GAZB is generated, skipping a quantization and coding process, otherwise, judging that the GAZB is not generated; a non-GAZB skipping cost is calculated through a distortion optimization algorithm, whether a non-GAZB coding process is skipped or not is judged, if the normal coding cost is larger than the non-GAZB skipping cost, the block is judged to be a pseudo all-zero block PAZB for skipping, and otherwise, the block is judged to be a non-all-zero block NAZB; and in the calculation process of the dependent quantization DQ, all-zero detection is carried out by counting context features of the CG so as to skip the coding process in which the CG is detected to be all-zero. According to the method, a machine learning technology is not used, the calculation complexity is reduced under the condition that the coding quality is ensured, and the coding speed is increased.

The invention provides an intra-frame coding block division method, a video coding method and electronic equipment, and the division method comprises the steps: determining a target direction template of a current to-be-coded block according to at least four texture direction reference templates obtained in advance at different angles; obtaining texture features of the current to-be-coded block according to the target direction template; the texture features comprise a first feature value for representing the texture complexity and the texture predictability of the current to-be-coded block, and / or a second feature value for representing the difference of the texture predictability between sub-blocks of the current to-be-coded block; and / or a third feature value representing the difference of texture predictability between the current to-be-coded block and the sub-block of the current to-be-coded block; and inputting the texture features into a pre-obtained decision tree model to obtain a mark indicating whether the current to-be-coded block is divided or not. The texture complexity and the texture predictability can be considered at the same time, the encoding process of the video encoder is accelerated, the encoding time is shortened, and the power consumption of the hardware video encoder is reduced.

This application discloses a coding-based polynomial commitment method suitable for blockchain scaling, which eliminates the need for trusted initialization and improves prover efficiency by reducing coding time. The polynomial commitment method specifically performs the following operations: A parameter generation algorithm takes security parameters and the number of polynomial variables as input and outputs a set of common parameters; the prover executes the polynomial commitment algorithm to commit to the target polynomial, taking the coefficients of the target polynomial and the common parameters as input and outputting the commitment value; a polynomial commitment opening algorithm ensures that the verifier is convinced that the target polynomial is correctly assigned at a certain set of points. The polynomial commitment opening algorithm is an interactive proof protocol involving provers and verifiers, taking the coefficients of the target polynomial as input to the prover, and the common parameters, commitment value, polynomial assignment points, and the assignment result of the target polynomial as common input to both the prover and verifier. The verifier outputs either "accept" or "reject".

The present invention relates to a lightweight image super-resolution learning method based on a diffusion model, and belongs to the field of image super-resolution technology. The method comprises the following steps: S1: forward process; S2: predicting noise; S3: sparse momentum; S4: training process; S5: reverse process. Through the technical solution of the present invention, the speed of image super-resolution can be improved, so that the diffusion model does not need to perform image denoising according to the set maximum time step in the reverse reasoning stage, which helps the diffusion model to optimize the prediction noise mode, and the sparse momentum method adopted can further reduce the parameters of the model, so that the model can achieve the performance of a dense network under a sparse network, significantly improving the speed and efficiency of image super-resolution, while maintaining high-precision output on the basis of reducing computing requirements.

The invention relates to a VVC fast CU division method based on integrated learning, which belongs to the field of video coding and comprises the following steps: the method is mainly divided into a first stage dominated by a random forest classifier and a second stage dominated by a residual network, in the first stage, the random forest classifier is used for predicting the division state of a coding tree unit, and in the second stage, the residual network is used for predicting the division state of the coding tree unit; and the output result is either segmented or not segmented. In the second stage, the pixel brightness value of a 32 * 32 coding unit is input into the trained residual network classifier. After the residual network is calculated, the sub-coding units are classified into one of 17 category labels, and each label represents a 32 * 32 coding unit division structure. The method can be used for coding under VVC intra-frame coding configuration, the coding time of VVC is remarkably shortened under the condition that the coding efficiency and the coding quality are hardly reduced, and the method can be applied to video conferences and other application scenes with high requirements for coding real-time performance.

The application discloses a video coding method, a video decoding method and related devices. The video coding method comprises: coding a video to obtain coded data; adding a preset syntax in the coded data to obtain a video bitstream, different values of the preset syntax being used to indicate whether a knowledge image is used for display, and / or whether a same content frame of the knowledge image is encoded by using a skip mode compulsorily, the knowledge image and the same content frame corresponding to the same original image content. The application can reduce coding delay.

The invention provides a battery management system, a coding method, equipment, a medium and a program product, the battery management system comprises a main board and at least two slave boards, each slave board comprises a coding circuit, and the coding circuit comprises a voltage drop sub-circuit and a potential acquisition sub-circuit; the mainboard comprises a coding input circuit and a coding output circuit; the voltage drop sub-circuits of the slave plates are sequentially connected in series, and the two ends of the series-connected circuit are connected with the coding input circuit and the coding output circuit respectively to form a loop. The coding output circuit is used for outputting electric energy to the voltage drop sub-circuit of the slave board; the potential acquisition sub-circuit of each slave board is used for acquiring the potential of the voltage drop sub-circuit and determining the code of the slave board according to the potential; wherein the code represents the unique identification of the slave plate. The potentials of the voltage drop sub-circuits of all the slave plates are different, the codes of the slave plates are determined by collecting the potentials of the voltage drop sub-circuits, synchronous coding of the multiple slave plates is achieved, the coding efficiency is improved, and the coding time of the battery management system is shortened.

The present invention discloses a VVC-SCC intra-frame coding method and device based on multi-stage irregular coding unit division, which relates to the field of video coding, including: designing and training a coding unit division prediction model based on a multi-stage convolutional neural network; secondly, predicting the input video frame through the trained coding unit division prediction model, and outputting the prediction probability of the mode type of all CUs in the current frame; finally, setting different thresholds for different mode categories, guiding the VVC-SCC encoder to encode according to the prediction probability and the corresponding threshold, thereby skipping unnecessary mode category traversal. The present invention solves the problems of long encoding time and high encoder calculation complexity of the existing VVC-SCC intra-frame coding method.

This invention relates to a desktop image acquisition and encoding method and apparatus, belonging to the field of cloud desktop technology. The specific technical solution is as follows: An independent acquisition and encoding thread is created; within the acquisition and encoding thread, the pre-configuration of the graphics card for acquisition and encoding is completed; within the acquisition and encoding thread, a cyclic acquisition and encoding process is started, sequentially executing acquisition parameter configuration, calling the graphics card acquisition interface to complete desktop image acquisition, acquisition result processing, image resource registration to the graphics card encoder, calling the graphics card encoding interface to complete image encoding, encoding result processing, and bitstream extraction and transmission operations; after the acquisition and encoding task is completed, the cyclic acquisition and encoding process is terminated, the acquisition session and encoder session are destroyed, the corresponding resources are released, and the acquisition and encoding thread is reclaimed. This disclosure can improve the desktop rendering frame rate and image acquisition frame rate.

The present invention provides a Turbo encoding method and device, electronic device and storage medium, wherein the method comprises: encoding the serial output of the member encoder in the Turbo encoder in one dimension of the bit stream, extracting the time information, and based on the time information, converting the serial output of the member encoder into a two-dimensional generator matrix of the member encoder; based on the characteristics of the generator polynomial, converting the two-dimensional generator matrix into a plurality of partition matrices; based on each of the partition matrices and their corresponding preset lookup tables, parallel encoding the input information and then outputting it; the preset lookup table is a table listing all matching input vectors and output vectors corresponding to the partition matrices. The present invention obtains the encoding results of multiple consecutive input information at the same time through limited and few steps, greatly reducing power consumption, shortening encoding time, and using storage resources in exchange for increased encoding speed.

The invention requests to protect a video coding CU block classification method based on a lightweight deep learning model, and is suitable for the field of video coding. The method comprises the following steps: firstly, carrying out block processing on an input image, dividing the input image into a plurality of 128 * 128-pixel image blocks, and extracting brightness information of each image block; after normalization processing, brightness data is used as input of a deep learning model and is used for generating classification probabilities of multiple categories. For the subunits of each image block, the probabilities of different categories are compared pixel by pixel, and a category label with the highest probability is allocated to the subunits. And then, based on the classification result of the subunits, counting the number of each category in each CU block, and allocating a corresponding type to the CU block according to statistical information. The CU block can belong to multiple categories simultaneously, and the final category of the CU block is determined through bitwise operation. According to the method, through accurate CU block classification, the coding efficiency is improved, data support is provided for subsequent mode division and selection, and the method is suitable for video coding application scenes with high real-time performance requirements.

The invention relates to a VVC fast intra-frame coding mode decision-making method based on machine learning, which belongs to the field of video coding and comprises the following steps of: acquiring texture information, sub-block texture information and field information of a current to-be-coded CU (Control Unit) for LightGBM (LightGBM) model prediction; the method comprises the following steps: firstly, predicting probabilities of three categories by using a trained LightGBM model, wherein the three categories are respectively 0 (angle prediction mode), 1 (ISP mode) and 2 (MIP mode); comparing the probability with a threshold value, and removing the category of which the probability is lower than the threshold value, so as to reduce the number of candidate modes of which the complete rate distortion optimization process needs to be performed; in addition, under the condition that the category 0 is reserved, the probability of 67 angle prediction modes is further predicted by using another LightGBM model, and the prediction mode with lower probability is removed. According to the method, on the premise of ensuring the video quality, the intra-frame coding time of H.266 / VVC can be remarkably saved, and the method is suitable for video coding application scenes with high real-time performance requirements.

The invention relates to a VVC fast CU division method based on ensemble learning, which belongs to the field of video coding and comprises the following steps of: initializing a given coding unit (CU), and starting a coding process from the deepest level; then, feature extraction is carried out; the feature attribute value of the current CU is calculated; and model prediction: loading a pre-trained integrated learning classifier (i = 0, 1, 2), carrying out division prediction on the current CU by using the pre-trained integrated learning classifier, and then carrying out decision processing. And once the coding of the current CU is completed, moving to the next CU in the list and repeating the steps. Through the series of steps, the algorithm can systematically perform division decision on each CU so as to optimize the video coding process. The method can be used for coding under VVC intra-frame coding configuration, the coding time of VVC is remarkably shortened under the condition that the coding efficiency and the coding quality are hardly reduced, and the method can be applied to video conferences and other application scenes with high requirements for coding real-time performance.

The application discloses a VVC intraframe coding division method based on LightGBM, and has the characteristics that the coding features of a CU to be divided are acquired; the rate-distortion cost of other division modes of the CU to be divided is acquired; the coding features of the CU to be divided are input into a horizontal LightGBM model and a vertical LightGBM model which have been trained in advance to obtain a horizontal prediction value and a vertical prediction value; if the horizontal prediction value or the vertical prediction value is less than a preset threshold, the current division mode is skipped; if the horizontal prediction value or the vertical prediction value is greater than or equal to the preset threshold, the division mode with the minimum rate-distortion cost is selected as the optimal division mode of the CU to be divided; and the advantage is that the horizontal LightGBM model and the vertical LightGBM model are used to predict the CU to be divided, and whether the coding unit needs to be horizontally or vertically divided in a ternary tree is judged through the horizontal prediction value and the vertical prediction value, so that the rate-distortion cost calculation process of the horizontal or vertical division mode of the ternary tree in the coding process is skipped, and the coding speed is improved.