2055 results about "Compression method" patented technology

The invention discloses a real-time image video compression method for dynamic frame screening, and relates to the technical field of data compression and transmission, and the method comprises the steps: obtaining a power transmission line monitoring video stream, carrying out the importance analysis of a plurality of continuous frames through a multi-dimensional importance evaluation index, and generating an importance index; network bandwidth and computing power information are monitored in real time, and a screening threshold value is dynamically adjusted; and positioning the key frame and the redundant frame according to the comparison of the importance index and the screening threshold. The key frame is preferentially transmitted after being encoded by a preset encoder, and the encoder retains complete image information; and the redundant frame is subjected to lightweight compression or skipping processing through the differential processing module, and is transmitted after the key frame is transmitted. The technical problem that the stability and the real-time performance of video transmission are affected due to insufficient network bandwidth and equipment computing power resources in the prior art is solved, and the technical effects of reducing unnecessary data transmission and improving the real-time performance and the stability of video transmission are achieved.

The invention relates to the technical field of wind power generation equipment data processing. The invention provides a time sequence database data compression method, system and device and a storage medium. The method comprises the steps of dynamically partitioning time sequence data based on a preset time window; hybrid coding compression is performed on each data block, and a composite strategy of difference coding and run length coding is adopted for numerical data; a metadata index of the compression blocks is established, the time range, the data feature statistics and the compression parameters of each data block are recorded, and the metadata index comprises extreme value distribution, variance features and data fluctuation frequency indexes; and dynamically adjusting a compression strategy according to a historical data feature analysis result, predicting data fluctuation modes of different equipment sensors through a machine learning model, and automatically selecting an optimal coding combination and compression granularity for subsequent data blocks. The problems that when wind power plant time sequence data are processed through an existing method, time sequence characteristics are difficult to adapt, the storage cost is high and the query efficiency is low are solved.

The invention discloses a large model reasoning efficiency dynamic optimization and hardware sensing compression method. The method comprises the following five steps: S1, generating an input complexity signal representing calculation complexity; s2, synchronously monitoring a hardware resource index of the operation platform, and generating a hardware state signal reflecting a real-time load; s3, inputting the input complexity signal and the hardware state signal into a dynamic strategy selector, and generating a compression control signal through a pre-trained decision model; s4, according to the compression control signal, dynamic reconfiguration operation is executed on the large model weight and the activation value of the current reasoning task; and S5, performing reasoning calculation by using the reconfigured large model, and feeding back a hardware resource index to the step S2 in real time in the calculation process to form a closed-loop optimization link. According to the large model reasoning efficiency dynamic optimization and hardware perception compression method, the problems of low resource utilization rate, delay fluctuation and energy efficiency imbalance caused by a static compression method in dynamic input and heterogeneous hardware environments can be solved.

The present disclosure provides a variable-bit-rate image compression method and system, an apparatus, a terminal, and a storage medium. The variable-bit-rate image compression method includes: obtaining an initial feature map from a to-be-encoded image; quantizing the initial feature map by a dead-zone quantizer; performing entropy encoding on the quantized feature map and hyper-prior information to obtain a compressed bit-stream; performing entropy decoding on the compressed bit-stream, and recovering quantized hyper-prior information and the quantized feature map; performing inverse quantization on the quantized feature map to obtain a reconstructed feature map; obtaining a reconstructed image from the reconstructed feature map; and adjusting quantization and inverse quantization parameters according to a target bit-rate or target distortion. The present disclosure provides a precise bit-rate control solution, makes the bit-rate of the compressed bit-stream better adapt to the dynamic change of a network bandwidth, and has an extremely high actual application value.

The invention discloses a multi-scale semantic guidance image compression method and system and a storage medium, and the method comprises the following steps: obtaining input image data, carrying out the preprocessing of an input image, and obtaining standardized image data; inputting the standardized image data into a pre-trained semantic segmentation network to generate a multi-scale semantic feature map and a semantic weight map corresponding to the multi-scale semantic feature map; a three-stage pyramid encoder is constructed, and the standardized image data is subjected to the following steps of: sampling under depth separable convolution to generate multi-scale features; the reversible neural network carries out nonlinear transformation on the multi-scale features; the multi-scale feature subjected to nonlinear transformation is decomposed into a low-frequency sub-band and a high-frequency sub-band through adaptive discrete wavelet transformation, dynamic selective state space modeling is executed on the high-frequency sub-band based on a semantic weight map, and a compressed code stream is generated; and inputting the compressed code stream into a decoder, decoding based on a lightweight Mama module, and reconstructing an image in combination with inverse wavelet transform and a semantic weight map.

The invention discloses a cue word compression method and device of a large language model and a medium. The method comprises the steps that redundant vocabularies and redundant statements are recognized and removed according to the size relation between the vocabulary importance score and the importance threshold value of each vocabulary after original cue word segmentation; constructing a local syntax tree of each statement, and constructing a global analysis tree by the local syntax trees according to the dependency relationship among the cue words; calculating a local information entropy so as to align the LLM word segmentation device with nodes on the global analysis tree and adjust node values of the global analysis tree; trimming the global analysis tree through a recursive algorithm according to the adjusted node value, and generating a compression prompt corresponding to the prompt word; according to the method, the compression prompt is evaluated, the compression prompt is iteratively optimized according to an evaluation result, the cue word is reconstructed according to the optimized compression prompt, and compression of the cue word is achieved.

The embodiment of the invention provides a dynamic point cloud compression method and device based on time sequence perception, and the method comprises the steps: carrying out the sparse convolution processing of two continuous frames of input point cloud data, and obtaining point cloud features in a feature space, carrying out local and global feature extraction on representations of two continuous frames in the feature space, splicing and fusing to obtain a motion vector, and carrying out motion compensation on point cloud features of the previous frame by utilizing the accurate motion vector to obtain a prediction frame of the current frame; calculating a residual error between a prediction frame and an actual frame, carrying out compression coding to obtain a residual error tensor, adding the prediction frame and the residual error tensor to obtain a coarse-grained prediction feature, and carrying out time alignment and feature fusion on the coarse-grained prediction feature and the three-frame point cloud features stored in the buffer region to obtain a fused prediction feature; performing up-sampling reconstruction on the fused prediction features to obtain a final point cloud frame; according to the invention, the accuracy and efficiency of point cloud data compression can be effectively improved.

The invention provides a frequency difference compressor based on precision perception, a gradient compression method, equipment and a medium, which are used for carrying out data compression and transmission between a server and a client so as to reduce communication overhead in personalized federated learning, and relates to the technical field of data compression. The frequency difference compressor comprises an information bottleneck rarefaction unit, a frequency domain compression unit, a dynamic quantization unit and a differential coding unit. Non-key gradient redundant components of the original gradient data are removed through an information bottleneck rarefaction unit to generate sparse gradient data; generating a metadata packet containing the first N high-energy frequency domain coefficients and the positions and the number of the first N high-energy frequency domain coefficients through a frequency domain compression unit; performing adaptive bit width mapping on the high-precision floating point gradient data into low-order integer representation through a dynamic quantization unit to generate dynamic quantization gradient data; and the differential gradient is transmitted to the client through the differential coding unit, so that data compression and transmission are completed. The method solves the problems that the communication overhead is large and gradient information cannot be reserved as far as possible in gradient transmission.

The invention discloses a weak network environment video compression method based on deep learning, and relates to the technical field of video compression, and the method comprises the following steps: 1, carrying out the feature extraction of video content through a deep learning model; 2, dynamically adjusting video compression parameters according to the extracted features and real-time network conditions; step 3, compressing the video by using a self-adaptive coding strategy; according to the video compression method based on deep learning, the video compression efficiency and adaptability in a weak network environment are remarkably improved, the video content is subjected to feature extraction by using the deep learning model, key information in the video can be intelligently identified, such as a motion vector, texture complexity and scene change, and the video compression efficiency and adaptability are improved. Therefore, a more accurate compression strategy is realized, the compression ratio of the video data is optimized, and the smoothness and the stability of video playing are ensured to be kept under variable network conditions.

A unified platform for multi-modal data compression and decompression that enables efficient processing of correlated data streams while preserving relationships between different modalities. The platform employs a virtual management layer to analyze and route input streams, implementing correlation analysis to identify temporal and spatial relationships between streams. Multiple compression methods, including neural network-based approaches, are utilized to compress data sets while maintaining cross-modal dependencies. A neural upsampling system leverages learned correlations between streams to enhance reconstruction quality. The platform includes a synchronization manager that maintains temporal alignment and relationship preservation throughout processing. By integrating correlation-aware compression with neural upsampling techniques, the platform provides comprehensive multi-modal compression capabilities while preserving critical relationships between different data types. The system is particularly suited for applications involving synchronized audio-visual data, sensor streams, and other multi-modal content.

The invention provides an image coding and decoding method and a compression method for machine and human vision, and the method mainly comprises the steps: obtaining a first feature map and a second feature of a to-be-coded image, and obtaining the super-prior information of the first feature map; obtaining quantization step sizes of the to-be-coded image under different machine vision tasks, and performing quantization to obtain a corresponding feature map; modeling the feature map into Gaussian distribution, and predicting a corresponding mean value and variance through an entropy model; coding layer by layer according to the distribution and probability of each layer of feature map to obtain a feature map binary code stream; and combining the super-prior information binary code stream and the feature map binary code stream to obtain a compressed image binary code rate. According to the method, the quantization step size of feature map quantization is adaptively adjusted, transmission feature code streams for human vision and different machine vision tasks are formed, information in the feature map can be more efficiently utilized, the transmission code rate is reduced, meanwhile, the performance of the machine vision tasks is guaranteed, and meanwhile, the rate distortion performance oriented to human vision is balanced.

The invention provides a context compression method based on multi-round dialogue intention graph construction, which comprises the following steps: S1, dialogue data acquisition and preprocessing: carrying out natural language processing on each dialogue unit; s2, intention atlas construction is achieved through node design and edge design, and each node comprises original text content and structured semantic information; s3, carrying out context compression and graph structure cutting, and only retaining sub-graphs forming a core semantic link; s4, dynamic context management and topic jump processing: in a multi-topic dialogue, when a user jumps or switches to a new topic, a system records a sub-graph of a current active topic, and contextual nodes of an inactive topic are frozen; s5, context sequence generation and model input: linearizing node contents in the cut sub-graph according to a dependent link sequence to generate a compressed context sequence, and transmitting the context sequence and current user input to a large language model for reasoning; and S6, continuous updating and feedback optimization are carried out.

The invention provides a train forward clearance target detection method and system based on point cloud feature aggregation enhancement, and belongs to the technical field of train forward clearance target detection.The train forward clearance target detection method comprises the steps that vehicle-mounted point cloud data are obtained, and a scene point cloud data set is constructed; dividing sparse point clouds into regular voxel grids, converting point cloud information into compact feature representation by voxel feature coding, and extracting multi-scale voxel features by using 3D sparse convolution layer-by-layer downsampling; carrying out sampling, grouping and feature extraction on the extracted multi-level voxel features by using set sampling, and aggregating the multi-scale voxel features to a voxel center point; mapping the extracted layered multi-scale voxel features into BEV feature maps by adopting a completely sparse height compression method, and fusing the BEV feature maps with different resolutions through 2D sparse convolution processing and jump connection; the mass center offset loss of a 3D regression frame is introduced to optimize the multitask loss, and a detection head is guided to realize train forward clearance target classification and three-dimensional bounding box regression.

A unified platform for multi-type data compression and decompression is disclosed. The platform employs a virtual management layer to receive, organize, and route input data to corresponding compression subsystems. Multiple compression methods, including homomorphic encryption-based techniques, are utilized to compress data sets while maintaining data privacy. A data manager associates and manages related data sets throughout the compression and decompression processes. Compressed data is routed to appropriate decompression subsystems, where it is decompressed and reconstructed using advanced techniques, such as neural upsampling, to recover lost information and enhance data quality. The platform supports various data types and compression methods, enabling efficient and secure compression and decompression of data. By integrating homomorphic encryption and data reconstruction techniques, the platform provides a comprehensive solution for data compression and decompression while preserving data privacy and enhancing data quality.

The invention discloses a cue word compression method and system based on TF-IDF and cross entropy, belongs to the technical field of large model cue word compression, and aims to solve the problems that redundant information is introduced into long cue words, the model efficiency is reduced and the cost is increased. To-be-compressed content is divided into sentences at the sentence level and then converted into embedded vectors, and the Euclidean distance is calculated in combination with problem vectors so as to screen related sentences; calculating a TF-IDF value at the word level through a word frequency and an inverse document frequency to extract keywords and recombine sentences; and selecting a reference model and a basic model at the Token level, identifying the key Token based on a cross entropy loss difference value, and splicing the key Token in sequence to generate a compressed cue word. According to the method, a complex calculation structure is avoided, the inference efficiency is improved while the semantic integrity is maintained, and the resource consumption is reduced.

The invention relates to the technical field of data compression, in particular to an intelligent data compression method and system based on multi-protocol heterogeneous equipment interconnection. The method comprises the following steps: when it is detected that a new heterogeneous device applies to join a network, carrying out communication protocol real-time analysis and protocol conversion adaptation to obtain a heterogeneous device adaptation protocol; carrying out multi-device data stream receiving according to a heterogeneous device adaptation protocol, and generating data streams of a plurality of time windows; adaptive coding and multi-level compression are carried out on the data streams of the multiple time windows, and a multi-device adaptive compression mechanism is constructed; identifying compression load characteristics of each heterogeneous device, and performing dynamic task allocation to obtain an intelligent task allocation strategy; and driving real-time data intelligent compression operation based on an intelligent task allocation strategy and a multi-device adaptive compression mechanism. According to the invention, efficient and adaptive data compression of different devices is realized, and the data transmission efficiency and quality are improved.

The invention belongs to the field of computer vision, and particularly relates to a DETR target detection model compression method based on combination of pruning and knowledge distillation, and the method comprises the steps: calculating an attention mechanism of each layer of a Transform structure, and constructing a mask by using the attention mechanism; performing adaptive pruning on parameters of a linear layer and an attention layer of the Transform structure by using a mask to obtain a first compression model of the original model; taking a to-be-compressed encoder with a Transform structure as a student network, and taking the original model as a teacher network to carry out distillation learning; the student network performs distillation updating according to the global distillation loss and the intersection distillation loss to complete compression; according to the method, the calculated amount of the model can be remarkably reduced and the reasoning speed of the model can be greatly improved on the premise that the model does not have obvious precision glide.

There is provided a computer-implemented method for learned video compression, which includes processing a current frame (xt) and previously decoded frame ({circumflex over (x)}t−1) of a video data using a motion estimation model to estimate a motion vector (vt) for every pixel, compressing the motion vector (vt) and reconstructing the motion vector (vt) to a reconstructed motion vector ({circumflex over (v)}t), applying an enhanced context mining (ECM) model to obtain enhanced context ({umlaut over (C)}E) from the reconstructed motion vector ({circumflex over (v)}t) and previously decoded frame feature (x̆t−1), compressing the current frame (xt) with the assistance of the enhanced context ({umlaut over (C)}E) to obtain a reconstructed frame ({circumflex over (x)}t′), and providing the reconstructed frame ({circumflex over (x)}t′) to a post-enhancement backend network to obtain a high-resolution frame ({circumflex over (x)}t).

The invention discloses a compression and decompression method and device for business data, equipment and a medium, and the compression method comprises the steps: obtaining to-be-compressed target business data, and carrying out fragmentation compression to obtain a first compressed file comprising a first file header, a tail file header and a plurality of first compressed fragments; according to the fragmentation compression, the whole target business data can be segmented and compressed into a plurality of first compressed fragments, so that the time consumption of a decompression process is reduced; generating a second file header according to the first file header, the initial position information of each first compressed fragment and the tail file header; and packaging the first compressed file and the second file header to obtain a target compressed file. The second file header can give the initial position information of each first compressed fragment, and the tail file header can give the offset information of each sub-file in the first compressed fragment, so that the compressed files can be efficiently decompressed. The technical scheme can be widely applied to the technical field of data processing.

The invention relates to the technical field of text processing, in particular to a text compression method, a text decompression method, a model training method, devices and equipment.The text compression method comprises the steps that a to-be-compressed target text is obtained, and the to-be-compressed target text is input into a feature extraction module of a text compression model; using a deep learning technology to capture semantic and syntactic features of the text, and expressing the extracted key information in the form of an embedded vector sequence; and a q-former quantization processing module is used for carrying out quantization processing on the embedded vector sequence, and the continuous embedded vector sequence is converted into a discrete token sequence, so that effective compression of text information is realized. By adopting a form of combining a feature extraction module and a q-former structure, the redundancy of text data can be remarkably reduced, and meanwhile, the high-quality restoration capability of an original text is kept.

The invention discloses an audio data compression method and device, electronic equipment and a storage medium, relates to the technical field of voice processing, can be applied to financial science and technology and medical health business scenarios, and comprises the following steps: obtaining target audio data to be compressed; and inputting the target audio data into an audio coding and decoding quantization compression model to obtain a reconstructed audio signal corresponding to the target audio data, the audio coding and decoding quantization compression model being obtained through audio adversarial training. In the model, feature extraction and compression can be performed on target audio data by using a hierarchical neural network of an encoder to obtain a low-dimensional potential feature vector; a residual vector quantization module performs discrete quantization processing on the low-dimensional potential feature vector through a multi-layer cascaded codebook to obtain a discrete quantization code; and performing audio waveform reduction processing on the discrete quantization code by using a decoder to obtain a reconstructed audio signal corresponding to the target audio data. According to the invention, audio high-fidelity compression can be realized, and audio reconstruction tone quality is improved.

The invention relates to the technical field of model compression, and provides a large model compression method and device, a task processing method and equipment and a storage medium, and the large model compression method comprises the steps: carrying out the layer-by-layer quantification of a linear layer of a to-be-compressed initial large model, and obtaining a first large model; the initial large model is a pre-trained large language model constructed based on an expert hybrid architecture; performing route calibration on each expert sub-model in the first large model to obtain a second large model; in the reasoning process of the second large model, based on the task type of a to-be-executed target task, the importance of each expert sub-model in the task type is evaluated; and performing dynamic pruning on each expert sub-model based on importance so as to compress the second big model. Through a compression mode of combining static quantification and dynamic pruning, on the basis of ensuring the model performance, the memory and calculation overhead required by large model reasoning can be reduced, and efficient operation of the large model on light-weight equipment with limited video memory resources is facilitated.

The invention provides a real-time video image compression method based on deep learning, and relates to the technical field of video image compression, and the method comprises the steps: carrying out the key feature recognition through employing an attention mechanism; performing convolution training optimization on the video image sample data set by using a deep learning network structure; a self-encoder structure is designed to carry out feature map encoding compression; a video image compression adaptive network is generated through series fusion; a real-time video image frame is collected for preprocessing, and feature compression processing is performed on a standard video image frame based on a video image compression adaptive network. According to the method and the device, the technical problem that the video compression quality is reduced due to the fact that the generalization ability is insufficient in the face of various scenes and the video compression strategy is difficult to adaptively adjust according to different scenes in the prior art can be solved, the adaptive network is constructed through the combination of deep learning and the auto-encoder, and the video compression quality is improved. And the video compression strategy is dynamically adjusted according to the contents of different video images, so that the video compression quality is improved.

The invention provides a data compression method and device, and relates to the technical field of data processing and data compression. The method comprises the following steps: acquiring to-be-compressed data, and acquiring a data category of the to-be-compressed data; determining a plurality of candidate compression strategies corresponding to the to-be-compressed data based on the data category; the importance degree of the to-be-compressed data is obtained, and the equipment state parameter of the current equipment is obtained; determining a target compression strategy and a compression level corresponding to the target compression strategy from the candidate compression strategies based on the equipment state parameters and the importance; and executing the target compression strategy on the to-be-compressed data according to the compression level to obtain compressed data. According to the method, the target compression strategy can be dynamically determined according to the data type, the data importance and the equipment state, high flexibility and adaptability are achieved, the data storage and processing efficiency is improved, good balance between the data quality and the storage space is ensured, and the method adapts to modern changeable equipment environments and data requirements.

The invention belongs to the technical field of computer graphics and real-time 3D scene reconstruction, and relates to a real-time compression and optimization method based on a 3D Gaussian sputtering (3DGS) model, which realizes efficient storage, transmission and rendering of a three-dimensional scene through residual vector quantization (RVQ) and a dynamic pruning technology, and comprises the following steps: S1, residual quantization vector quantization; s2, enhancing a dynamic pruning strategy; and S3, index compression and coding optimization. The invention provides a real-time 3D Gaussian sputtering (3D Gaussian sputtering) model compression system combining residual vector quantization and dynamic pruning, which can realize high-quality real-time rendering and extremely high storage efficiency. Through three core technologies of a vector quantization strategy, dynamic pruning strategy enhancement and index compression and coding optimization, on the premise that the rendering quality is guaranteed, the purposes that the model storage amount is reduced by more than 45 times, and the rendering speed is increased by 3 times are achieved.

The invention provides a context compression method based on query guidance. The context compression method comprises the following steps: filling query information and a context to be compressed into a dialogue template; respectively calculating first attention numerical values of a plurality of lexical elements in the context of the dialogue template; performing attention reconstruction on the context according to the first attention numerical values corresponding to the plurality of lexical elements; and compressing the context after the attention reconstruction to be within a preset target compression ratio. The invention further provides a context compression system based on query guidance, a storage medium and electronic equipment. Therefore, key information can be more efficiently reserved, a shorter context is input to the generative model, the reasoning efficiency of the generative model in a long context reasoning scene is improved, and the reasoning cost is reduced.

Methods and apparatus are provided for encoding and subsequent decoding of video data by using a learnt video specific dictionary for implicit neural compression. An implicit neural representation comprising a head layer and a tail layer is used with approximations to the head layer parameters. The approximations are determined with combinations of atoms of a learnt video specific dictionary. In one embodiment, the head layer approximations and tail layer parameters are encoded in a bitstream. The dictionary is learnt at the decoder. In another embodiment, a dictionary is sent in the bitstream. At decoding, a reconstructed image is computed using transmitted INR parameters.

Methods and systems implement a pleno-generation face video compression framework with bandwidth intelligence for generative models and compression. Heterogeneous-granularity facial description regularizes long-term dependencies between video frames and compensates for motion estimation errors caused by compact representations of motion information. A generative decoder reconstructs heterogeneous-granularity visual representations, providing auxiliary visual signals for attention-based recalibration of a GFVC-reconstructed face signal. A coarse-to-fine generation strategy avoids error accumulation. High efficiency for heterogeneous-granularity signal compression is achieved by two different entropy-based signal compression methods: heterogeneous-granularities feature representation from the key-reference frame as hyperpriors to optimize the entropy model for compressing heterogeneous-granularity feature from subsequent inter frames, and a feature difference operation for heterogeneous-granularities feature representation between key-reference and subsequent inter frames, such that the entropy model only compresses heterogeneous-granularities feature residual for redundancy reduction. Mixed-model dataset generation and training and model-specific dataset generation and training are also provided.

The invention relates to the technical field of video compression, in particular to an edge computing-oriented AI gateway video efficient compression method. The method comprises the steps of obtaining a to-be-compressed target video; clustering all images in a target video to be compressed to obtain each cluster, obtaining a feature index value corresponding to each image in the cluster according to a difference index value between each image in the cluster and other images in the cluster, obtaining a block number parameter corresponding to the cluster according to the feature index value, and obtaining a block number parameter corresponding to the cluster; according to the block number parameter corresponding to the clustering cluster, dividing each image in the corresponding clustering cluster to obtain each sub-block on each image in the clustering cluster; and according to the pixel difference between any two sub-blocks in the clustering cluster, carrying out coding replacement on the sub-blocks on the image in the clustering cluster to obtain a replaced video, and compressing the replaced video to obtain compressed data. And the compression rate can be improved while the high fidelity of the key content is ensured.

The invention provides an unstructured data compression method and device, equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction and clustering processing of a to-be-compressed unstructured data set, and obtaining a data segment clustering group; performing semantic region segmentation processing on each data fragment in the data fragment clustering group to obtain a semantic important region and a semantic non-important region; performing differential coding processing on the semantic non-important regions in the same clustering group to obtain differential coding data; and performing high-quality compression processing on the semantic important regions in the same clustering group, and performing merging processing on the compressed important semantic data and the difference coding data to obtain an unstructured data compression result. According to the method, semantic region segmentation and differential compression processing are performed on the unstructured data, so that the data compression efficiency can be effectively improved on the premise of ensuring the quality of important information, and an efficient solution is provided for storage and transmission of large-scale unstructured data.