129 results about "Volume (compression)" patented technology

The invention discloses a video stream processing method for dynamic Gaussian compression and adaptive code rate regulation, which is suitable for scenes such as virtual reality, augmented reality and three-dimensional video, and comprises the following steps: S1, Gaussian attribute modeling and initialization; s2, constructing a binary hash grid; s3, constructing a deformation prediction network; s4, designing a mask pruning mechanism; s5, entropy modeling and arithmetic coding and decoding module design; s6, model training; and S7, video stream transmission under multiple code rates. According to the method, a unified scheme combining Gaussian volume cloud coding and adaptive video transmission is proposed for the first time, the video data storage and transmission cost is remarkably reduced, and the comprehensive performance superior to that of an existing method is obtained on multiple real and synthetic data sets.

The invention discloses a neural radiation field rendering method based on dynamic hash coding, and the method comprises the steps: employing the feature sequence data as the input, calculating the density value and color value of each sampling point through the forward propagation of a neural network, carrying out the volume rendering integral operation according to the ray tracing principle in the direction of a ray, and obtaining the feature sequence data; judging a final color output result of the current pixel point; according to an error value between the color output result and a real image, updating a network parameter weight through a back propagation algorithm, and if the error value is greater than a convergence threshold, continuing to iterate the training process to adjust a feature coding strategy to obtain an optimized neural radiation field model parameter; and after the rendering performance configuration parameters are obtained, optimizing a storage allocation strategy of feature data through a memory pool management mechanism, and if the current memory occupancy rate exceeds a safety threshold, starting a data compression algorithm to reduce the storage space requirement, and obtaining a real-time rendering output result. According to the invention, high-quality real-time rendering of the dynamic scene is realized.

The invention provides a data processing method and a related device. In the data processing method provided by the application, perception data acquired by UE is divided into first target data and second target data, an object corresponding to the first target data has a corresponding projection in a reconstruction space, and an object corresponding to the second target data does not have a corresponding projection in the reconstruction space. The first target data and the second target data are compressed in different compression modes and uploaded to the base station, and the compression degree corresponding to the first target data is higher than that corresponding to the second target data, so that the data volume interacted with the base station is reduced, the communication efficiency in the environment reconstruction process is improved, and the bandwidth load is reduced.

The invention relates to the field of traffic storage optimization and traffic data compression, in particular to a multithreading-combined lossless compression method for fine-grained traffic, which comprises the following steps of: acquiring a traffic data file, extracting a header triple based on the traffic data file, generating an identifier according to the header triple, and transmitting the identifier to a server; aggregating the traffic data files with the same identifier to obtain data streams, and generating a sorted data stream size table according to the data volume of the data streams; creating threads according to system computing power, distributing the data flow to the thread with the minimum thread load according to the data flow size table and the thread load condition, and outputting a data flow distribution scheme; and performing fine-grained characterization and serialization on each data stream to obtain an integer sequence and a byte sequence, selecting a compression processing method according to redundancy characteristics of the integer sequence and the byte sequence, obtaining a redundancy-eliminated sequence, and writing the redundancy-eliminated sequence into a compressed file. The invention aims to provide high compression efficiency to reduce the storage cost while ensuring the data precision.

The present invention relates to the field of video processing technology, and in particular to a real-time video analysis method based on edge computing. The content includes: collecting raw video data, and preprocessing the raw video data to obtain an image frame sequence; segmenting the image frame sequence through an adaptive time slice adjustment mechanism to generate an adaptive time slice; based on the adaptive time slice, compressing and reconstructing through a dynamic feature-guided compression algorithm, extracting features from the obtained reconstruction matrix of the adaptive time slice, and obtaining a reconstructed feature matrix of the adaptive time slice; based on the reconstructed feature matrix of the adaptive time slice, performing video analysis. It solves the problem that the traditional time slice method usually adopts a fixed length and is difficult to cope with the dynamic changes of the video content; the existing compression algorithm is difficult to balance the reduction of the video data volume and the retention of key features during the compression process, resulting in a decrease in the quality of the compressed video and affecting the subsequent analysis accuracy.

The invention discloses a digital textbook editing and transmitting system with a multilayer architecture. The digital textbook editing and transmitting system comprises a virtual node modeling module, a lightweight storage module, a function editing module, a resource ecological module and a client module, the virtual node modeling is as follows: Slate is used to construct a virtual node model containing elements such as texts, pictures, formulas, interaction components and the like, and each node carries content attributes, hierarchical relationships and interaction instructions; the lightweight storage module is used for converting a virtual node into a JSON format through global-jsdom after a user completes editing, and classifying binary files in the node into resource nodes in an editor. The invention relates to the technical field of data transmission, adopts an AES encryption transmission technology, avoids the data leakage risk caused by plaintext transmission, and improves the data transmission efficiency. And the digital asset security of the publisher and the user is effectively protected. Through node compression storage (JSON format), the server side can classify and store the JSON data, the data volume is greatly reduced, the storage space multiplexing is improved, and the transmission efficiency is improved.

The invention relates to the technical field of machine learning, and discloses a feature disassembly and compression method and device, equipment and a storage medium, and the method comprises the steps: obtaining an initial feature of to-be-predicted image data, determining an expert activation weight corresponding to a downstream prediction task based on a hybrid expert network, the initial features are processed through a plurality of expert networks in the hybrid expert network to generate disassembled features, the plurality of expert networks are networks constrained through a low-rank matrix, the disassembled features are quantized to generate quantized features, entropy coding is carried out on the quantized features through a hyper-prior network, and compressed features are obtained. According to the method, the expert activation weight is dynamically adjusted along with task requirements, different task scenes are adapted, initial features are disassembled based on a plurality of expert networks, task irrelevant information is explicitly stripped, task key features are reserved, redundant data volume is reduced, quantized coding is performed on disassembled feature input, code stream self-adaption is realized, and bandwidth occupation is reduced.

The embodiment of the invention provides a tag data processing method, RFID middleware and a storage medium. The method comprises the following steps: receiving and caching tag data of a plurality of tags; compressing the label data according to a predetermined compression mode to obtain compressed data; wherein the compression mode is determined according to a data compression level and a label inventory rate; and sending the compressed data. According to the embodiment of the invention, the data processing efficiency is improved, the equipment resource occupancy rate, the data traffic and the bandwidth occupancy of network transmission of middleware at a high inventory rate are greatly reduced, and meanwhile, the mass data processing pressure of an upper platform is reduced. According to the embodiment of the invention, the platform side can receive total label data, and algorithm application development is facilitated. In addition, according to the embodiment of the invention, the packet number and the data compression level are adjusted according to different service requirements, and the method is suitable for service scene requirements with different data volume and time delay sensitivity.

The invention discloses a backup and recovery method, device, equipment and product of a native storage volume, and relates to the technical field of data processing.The method comprises the steps that a snapshot function is called through a container storage interface, a current native storage volume is subjected to snapshot capture, a new storage volume is generated, and the new storage volume stores the complete state of current data; partitioning and compressing the new storage volume to obtain compressed data blocks, the partitioning being used for distributed processing of data, and the compression being used for reducing the use of storage space; extracting key data blocks from the compressed data blocks according to the demand information; decompressing the key data block through a decompression algorithm to obtain a decompressed file block; the decompressed file block is written into the newly created blank storage volume to obtain a recovered storage volume, the recovered storage volume is re-mounted to the target system, data information in the recovered storage volume is the same as that in the new storage volume, and the backup and recovery adaptability under different requirement scenes is improved through the method.

The invention provides an FPGA-based universal JPEG grayscale image compression module. The FPGA-based universal JPEG grayscale image compression module comprises a grayscale data caching module, a grayscale data reading module, a one-dimensional DCT conversion module, a two-dimensional DCT conversion module, a quantization module, a zigzag scanning module, a DC coefficient coding module, an AC coefficient coding module, a JPEG data stream generation module and an output framing module. According to the method, the storage area occupied by the image data in the DDR3 is stipulated by configuring the initial storage address of the DDR3; by configuring image width and image height parameters, performing JPEG (Joint Photographic Experts Group) compression processing on any grayscale image which is smaller than the capacity of the DDR memory and has image height and image width which are positive multiples of 8; adjusting the speed of an image compression process by configuring a compression speed parameter; and the quality of the compressed image and the total data volume of the JPEG data stream are accurately controlled by configuring an image compression ratio parameter. The transmission efficiency of the remote sensing image is improved, and the occupied storage space is reduced; and the method can be conveniently and flexibly applied through parameterized configuration, and is easy to transplant and high in universality.

The invention belongs to the technical field related to volume video coding compression, and particularly relates to a multi-view video compression method and application thereof, and the method comprises the steps: initializing the information of each anchor point according to a video first frame; performing quantization and entropy coding on the current anchor point information of the partial anchor points by adopting a preset quantization offset parameter, and adding coding results to obtain Loss2; respectively adding corresponding quantitative offsets to current anchor point information of all anchor points, respectively and correspondingly inputting static and dynamic characteristics with offsets of each anchor point into mlp of neural Gaussian static and dynamic attributes to obtain static and dynamic attributes of each neural Gaussian of the anchor point, obtaining deformation attributes from the dynamic attributes, and obtaining deformation attributes from the deformation attributes; rendering based on the attribute information of each neural Gaussian to obtain a picture, and comparing the picture with a real picture to obtain Loss1; the mlp and all anchor point information are optimized through the two Loss, after optimization, the optimized anchor point information is quantized through Hash coding, entropy coding is conducted, and the coding result and the optimized mlp are compression results. The compression rate can be greatly improved.

The invention relates to the technical field of oral images, and discloses an oral panoramic image real-time compression transmission and 3D reconstruction cloud system. The system comprises an image acquisition module, a compression processing module, a cloud transmission module and a 3D reconstruction module. The image acquisition module acquires initial image data and pre-processes the initial image data to obtain target image data; the compression processing module generates a compression strategy through block coding, and when conditions are met, sub-compression units are divided, and dynamic bit allocation is optimized; the cloud transmission module extracts the compressed code streams, constructs real-time and delayed transmission queues in a classified manner, ranks the queues, verifies the integrity of the code streams to be verified and counts the complete number; and the 3D reconstruction module determines an effective data size according to the complete number and the compressed code streams which do not pass the verification, and starts a cloud reconstruction engine. The system improves the oral image processing efficiency and the 3D reconstruction quality through dynamic compression, intelligent transmission and precise verification, and is suitable for oral medical diagnosis scenes.

The invention discloses a radiation monitoring data compression and real-time transmission method and system, and relates to the technical field of data compression and transmission, and the method comprises the steps: collecting original radiation monitoring data, and carrying out the data preprocessing; carrying out lossless compression coding on the radiation monitoring data characteristics; the compressed data are divided into small segments, and the data are transmitted in a segmented mode; and after receiving the data, the receiving end decompresses the data according to the compression reverse operation. According to the invention, the transmission efficiency and reliability of radiation data under the condition of low bandwidth can be obviously improved. The compression algorithm can realize a data compression rate of at least 50% or more, and meanwhile, the integrity and precision of the data are kept. In addition, a real-time transmission mechanism ensures that data can be effectively transmitted under an extreme network condition, and the delay and packet loss rate of data transmission are remarkably reduced. The volume of radiation monitoring data can be significantly reduced without sacrificing the quality and integrity of the data.

A file system volume's space is internally allocated as a set of containers, with each container corresponding to a different subset of storage clusters. The set of containers includes a first container corresponding to a first subset of storage clusters storing first data that is compressed according to a first set of compression attributes, a second container corresponding to a second subset of storage clusters storing second data that is uncompressed, and a third container corresponding to a third subset of storage clusters that are free. Based on determining that the second data of the second container is to be compressed, compressed second data is created based on a second set of compression attributes. The second set of compression attributes is different than the first set of compression attributes. The compressed second data is written to the third subset of storage clusters of the third container.

The invention discloses a quantum computing gate circuit expression and compression method and a medium, and relates to the technical field of quantum computing, and the method comprises the steps: setting a conversion expression character between a user side and a quantum computing cloud platform, or setting the conversion expression character between the quantum computing cloud platform and a terminal quantum computer for unifying quantum languages; the conversion expression character is divided into three segments which are an operation code, a bit code and a parameter code in sequence, the operation code refers to quantum gate operation, the bit code refers to a quantum bit number, and the parameter code refers to a floating-point number related to the operation code; the core of the method is that the file volume is compressed and the quantum language is unified, so that the problem of transmission efficiency of existing codes among different terminals is solved.

The invention discloses an image compression method based on adaptive quadtree segmentation and standard deviation optimization, and provides an efficient compression scheme driven by dynamic statistics aiming at the problems of over-segmentation, low calculation efficiency and detail loss under a high compression rate in a traditional quadtree compression algorithm. Specifically, the compression ratio and the reconstruction quality are effectively balanced by calculating the RGB channel standard deviation of an image region in real time and combining region area weighted income analysis to adaptively control the recursive segmentation depth; an area weighted error constraint mechanism is introduced, segmentation of a large-size low-variance region is terminated preferentially, and the redundant data volume is remarkably reduced; a standard deviation-driven priority ranking strategy is adopted, fine processing is performed on high-complexity sub-regions, and rapid approximation is realized by combining mean color filling, so that the algorithm efficiency is optimized while the peak signal-to-noise ratio is ensured. Through the synergistic effect of the technologies, on the premise that high-frequency details of the image are reserved, calculation complexity and storage requirements are greatly reduced, the method is suitable for real-time image transmission, mobile terminal processing and low-bandwidth communication scenes, and a reliable solution is provided for high-precision and low-delay compression requirements.

This invention discloses an efficient RNA data preprocessing method to address the problems of low processing efficiency and low information density in high-throughput sequencing data. Its core steps include: (1) introducing a parallel processing scheme for high-throughput sequence data, rapidly mapping RNA-seq data to a reference genome to generate a BAM file; (2) extracting base sequences and expression levels and storing them as compact PKL format files; (3) extracting all exon position information by parsing the genome annotation file; (4) combining multi-sample expression level data to screen for highly variable exons and constructing a high-information-density feature list based on the sample set; and (5) accurately extracting target sequences from the preprocessed file based on this list. Compared to traditional methods, this innovative approach achieves triple optimization: full-process parallel processing for accelerated computation, high-compression data storage, and adaptive feature selection. Processing speed is increased by 3-5 times, and data volume is reduced by more than 90%, making it suitable for high-throughput RNA-seq data analysis with large sample sizes.

The invention provides a configuration method, a use method and a configuration system for a compression algorithm template of a storage device, and the configuration method comprises the steps: receiving host data, analyzing the host data according to a preset carry count system, a preset bit number and a preset numerical sequence, obtaining a corresponding data mode, and storing the data mode in a storage device. Splitting the host data into a plurality of host sub-data according to a data mode; calling all preset compression algorithms to compress the host sub-data of each data mode to obtain corresponding compressed data, and calculating a corresponding compression rate; for each data mode, acquiring a compression algorithm with the lowest compression rate, recording the compression algorithm as an optimal compression algorithm, and matching each data mode with the corresponding optimal compression algorithm to generate a compression algorithm template; wherein the compression ratio represents the ratio of the data volume of the compressed data to the data volume of the corresponding host data. According to the invention, the read-write time of the storage device on a large amount of repeated data can be reduced, and the service life of the storage device is reduced.

The present application relates to a compression method for zero-trust network access technology. The method includes: when there is data update in the system, judging the type of data to be updated to obtain a judgment result; when the updated data in the system is a file, when the updated file meets the preset access volume condition, triggering a compression mechanism to compress the updated file; when the updated data in the system is a policy or a user, calculating the correlation degree between the updated policy or user and the file, and judging whether to trigger the compression mechanism to compress the file corresponding to the updated policy or user according to the correlation degree. The present application can identify the type of updated data, apply corresponding judgment criteria, obtain a reasonable judgment result for the updated data, intelligently compress and cache the data that meets the criteria, and save as much network bandwidth resource as possible with as little computing power as possible.

The invention provides a hardware-oriented matrix data lossless compression and decompression method and a hardware-oriented matrix data lossless compression and decompression system. The method comprises the following steps: S1, judging a compression process or a decompression process; if compression is carried out, S2 is carried out; if decompression is carried out, S3 is carried out; s2, executing a matrix data lossless compression method; and S3, executing the matrix data lossless decompression method. The system implements the method in NNA hardware, and comprises a plurality of parallel hardware modules corresponding to the lossless compression method; the lossless decompression method corresponds to a plurality of parallel hardware modules; after the NNA generates FMs data, the FMs data are firstly placed in the DRAM, the lossless data compression hardware modules compress the data of different layers / channels in parallel, and then the compressed data are transmitted to the large-capacity SRAM; when the FMs data need to be used, the data of different layers / channels in the SRAM are decompressed in parallel to the DRAM through a plurality of lossless data decompression hardware modules; in the process, the data volume of the FMs is reduced through the data compression module.

The invention provides a data storage and reading method, device and equipment, and the data storage method comprises the steps: extracting the features of to-be-stored data, and applying for an index storage position in a traversed current data volume according to a level sequence from a higher level to a lower level, when it is determined that first to-be-stored data which does not meet the data similar compression condition exists, the first to-be-stored data is written into an applied target storage position, and the target storage position is written into an index storage position; when it is determined that second to-be-stored data meeting the data similar compression condition exists, the second to-be-stored data is compressed, the to-be-stored data is updated, the index storage position applied by the to-be-stored data in the next-level data volume is written into the current data volume, and if the level of the current data volume is the lowest level, the next-level data volume is created, and a data storage instruction is triggered. Through the method, the to-be-stored data is stored after being subjected to multi-stage compression based on the multi-stage data volume, so that the compression rate of the data is improved.

The invention provides a data compression method and device, electronic equipment and a storage medium, and relates to the technical field of data compression. Comprising the following steps: pre-coding brightness correction data corresponding to a pixel block in a target area, and determining an initial compression parameter and data damage information corresponding to the pixel block; determining a target compression parameter corresponding to the pixel block according to the data damage information corresponding to the pixel block and the initial compression parameter; and based on the target compression parameter corresponding to the pixel block, compressing the brightness correction data corresponding to the pixel block. Therefore, while the brightness correction data is compressed and the data volume is reduced, the brightness uniformity between different pixel blocks is ensured after the brightness of the screen is corrected based on the compressed brightness correction data.

The present invention relates to the technical field of data processing, and specifically provides a method and system for compressing and reconstructing Internet of Things data based on deep compressive sensing. The collected data of several sensors is used as the original data, and compressive sensing is performed to obtain compressed data; the compressed data is decoded and reconstructed; the compressed data is mapped to the original space to obtain initialization data, which is input into the residual decoder to obtain decoded data; the decoded data of the residual decoder is mapped to a high-dimensional embedding space for data reconstruction to obtain output data; the output data is mapped to the original space through a fully connected layer to obtain reconstructed data, and the reconstructed data is the original data. A residual sensing block is used to adaptively generate a learnable measurement matrix, reducing the data transmission volume in the Internet of Things and improving the data reconstruction accuracy. The residual decoder and the data reconstruction decoder are combined to complete the reconstruction of Internet of Things data by extracting key features from the compressed data.

Embodiments of the present application provide a BIM model value-driven three-dimensional unit compression method and system, which comprises: obtaining an original BIM model containing geometric information and material information and a general compression target; and calculating a component value score of each component in the original BIM model; based on the original BIM model and the component value score, performing voxelization processing and compression unit division to obtain a plurality of compression units; independently compressing and adaptively entropy encoding the plurality of compression units respectively to generate code streams corresponding to the plurality of compression units respectively; collecting the code streams corresponding to the plurality of compression units respectively to obtain a total volume of the plurality of compression units; based on the total volume and the general compression target, determining an encoding parameter to complete compression and obtain final code streams corresponding to the plurality of compression units respectively; and storing the final code streams to obtain a final compression file. The above scheme can accurately balance the compression ratio and data fidelity, and improve the encoding and decoding efficiency.

The invention discloses a reverse debugging increment recording method based on dynamic layering and predictive filtering, which comprises the following steps of: analyzing a binary file division region of a target program, establishing a global region mapping table among an address range, a region type and debugging attributes, executing the binary file by a reverse debugger, constructing a first mapping table for modifying a record circular queue, according to the global region mapping table, performing differentiation recording on the region to form a first record, a stack frame snapshot, a second record and a register block snapshot, and performing repeated coverage filtering to reduce the recorded data volume to form an execution state track; and performing intelligent compression on stack frame snapshots, heap memories, global variable records, register snapshots and variable values in the execution state trajectory, and finally forming a low-overhead and accurate execution state trajectory to support reverse debugging backtracking.

The present invention belongs to the field of data processing technology, and specifically relates to a data information processing method and system based on big data. The method includes: constructing a Huffman tree based on frequency statistics of all data in the data information; obtaining a special layer in which the increase in data volume is less than the decrease in data volume when the path code from the root node to the branch node is replaced with a fixed-length code of the sequence number of the branch node in the layer; and subsequently encoding all data through the Huffman tree and the special layer. For target data corresponding to the special layer, the branch node of the leaf node corresponding to the target data in the special layer is used as a guide node, and the fixed-length code of the guide node in the special layer is spliced with the path code from the guide node to the leaf node corresponding to the target data to obtain the encoding result of the target data. The present invention improves the compression rate of the data information compression result.

The invention discloses a map compression method and device and a computer readable storage medium. The method comprises the following steps: acquiring an original map, wherein the original map comprises a plurality of image blocks; compressing the image block by using a map compression algorithm to obtain a first compression result corresponding to the image block; multiplexing the first compression result corresponding to the target image block, and determining a second compression result corresponding to the image block; determining a first compression evaluation index corresponding to the first compression result and a second compression evaluation index corresponding to the second compression result based on the pixel distribution condition of the image block; and based on the first compression evaluation index and the second compression evaluation index, determining a target compression result corresponding to the image block from the first compression result and the second compression result so as to compress the original map. According to the method and the device, the technical problem that the volume of the map obtained by compressing the map is relatively large in the prior art is solved.

Three-dimensional sensing data is transferred efficiently by applying compressed sensing using dictionary learning. Conventional image-based lossy compression improves a feature according to which power is concentrated on low-frequency components and deterioration of three-dimensional information increases. By changing the decimation rates for the depth information and the color information and using a dictionary vector created from the other restoration result for restoration, the original result is reconstructed using a small data volume.

The application relates to the technical field of CAE preprocessing and cloud cooperation, and discloses a lightweight CAE geometry engine and a cloud real-time cooperation method and system. A cloud server acquires an original CAE geometry model, performs topological simplification and data compression on the original CAE geometry model, and generates a lightweight geometry data file containing multiple resolution levels; the cloud server pushes the received difference data packet to other online cooperation terminals; the other cooperation terminals parse the unique version number carried by the difference data packet through a locally loaded WebAssembly geometry engine module, and perform operation replay updating on the local geometry model according to the differential data contained in the difference data packet. According to the scheme, the original CAE model data volume is reduced by 90%-95%, the WebAssembly engine realizes a 100,000-face model rendering frame rate of 30-60 fps, and the operation response delay is less than or equal to 100 ms, thereby breaking through the performance bottleneck of browser-side geometry processing.