153 results about "Lossless compression" patented technology

The invention relates to the technical field of computer vision and image recognition, in particular to an image recognition method based on edge computing, which comprises the following steps: dynamically capturing an original image through a plurality of edge nodes, rejecting redundant regions through a multi-modal perception triggering mechanism, and establishing a cooperative processing group. Illumination equalization, noise filtering and resolution self-adaptive compression tasks are distributed according to dynamic role election, a standardized preprocessed image is generated, a lightweight convolutional neural network is operated in parallel to extract a dual-channel feature vector, and after entropy coding lossless compression and equipment identity tag and time sequence stamp attachment, the dual-channel feature vector is transmitted to a cloud end by adopting a lightweight encryption protocol. The cloud end analyzes the data packet, reconstructs a feature topological graph based on space-time relevance, loads a depth residual error recognition model to execute feature fusion and classification decision, feeds back and updates the weight of an edge node model, solves the problems of low collaborative efficiency and feature distortion, and improves the efficiency and precision of image recognition.

The invention provides a lossless compression method and decompression method for power grid waveform data, equipment, a medium and a program product, and the method comprises the steps: carrying out the periodic feature analysis of the original sampling data of a power grid waveform, so as to determine the periodic structure of the original sampling data, and carrying out the segmentation processing of the original sampling data based on the periodic structure; for the sampling data in each segment, extracting differential information between adjacent sampling points to form a differential data sequence; dynamically determining a corresponding quantization parameter based on the statistical distribution characteristic of the differential data sequence, and performing adaptive quantization processing on the differential data sequence to generate quantized differential data; and entropy coding processing is carried out on the quantized differential data to generate compressed data used for representing the original sampling data, and characteristic parameters used for reconstructing the original sampling data are stored in the compressed data in an associated mode. Unification of high compression ratio, low power consumption and strict lossless reconstruction is realized, and the application requirements of long-term monitoring and remote transmission of the edge side can be met.

The invention provides a large model KV precision lossless compression method and system, and relates to the field of data processing, and the method comprises the steps: obtaining key value cache data generated in a large language model reasoning process; performing high-dimensional feature extraction and similarity analysis on the key value cache data to generate a corresponding hash signature; based on the Hash signature, performing physical reordering on the key value cache data, so that the key value cache data with similar characteristics are adjacently arranged in a physical storage space; transmitting the reordered key value cache data to an intelligent network card through a high-speed interconnection interface; and calling an integrated hardware lossless compression module through the intelligent network card, and performing streaming compression on the reordered key value cache data. According to the technical scheme, the hardware unloading and assembly line technology is utilized, transmission bandwidth occupation and system overall delay are remarkably reduced, and key technical support is provided for efficient reasoning of a large model in a resource-limited edge environment.

The invention discloses a lossless compression storage method and system based on heterogeneous data in a credential environment, and the method comprises the steps: firstly, extracting a mapping relation between a file type and a structural feature through scanning a target file, carrying out the preliminary lossless compression after classification and grouping, and further employing a sliding window matching and sequence matching algorithm to optimize compressed data for a redundancy mode, and then generating a metadata structure containing a check code and a compression parameter, packaging the metadata structure into a data packet supporting cross-platform transmission, finally transmitting the data packet to a target storage device through a secure write-in protocol, and verifying data consistency and recovering path feasibility by using the check code. According to the method, the data compression efficiency is remarkably improved, the compatibility of cross-platform transmission and the integrity of stored data are ensured, and efficient and reliable technical support is provided for complex electronic file processing.

Raytracing and pathtracing systems use vertex compression including per-component shifts, unsigned arithmetic deltas, and base vertex shortening. Topology encodings include enhanced explicit vertex indexing and implicit triangle strip based indexing including left, right, start tokens, turn back, and degenerate tokens and including rotation. Substantial lossless compression ratio increases are realized, especially for highly quantized vertex data.

The invention relates to a video lossless compression transmission and storage method and device. The method comprises the following steps: analyzing an obtained video frame sequence; executing quality pre-verification, and if yes, determining that the video frame sequence is a first frame sequence; based on the scene detection target, determining a target attention area in the plurality of first sequence frames; selecting a plurality of target feature frames from the plurality of first sequence frames to execute importance evaluation, and determining corresponding feature levels; mapping the plurality of target feature frames into a one-dimensional pixel sequence; dividing the one-dimensional pixel sequence into a plurality of pixel segments, and performing cross-frame aggregation on the plurality of pixel segments according to the feature level to obtain a plurality of feature sequences; and carrying out hierarchical compression coding on the plurality of feature sequences. By adopting the method, the video can be subjected to quality pre-verification, the target concerned area can be adaptively identified, and differential compression and priority transmission are performed, so that the transmission quality and the storage efficiency of the inspection video can be improved in a complex electromagnetic environment and a limited network condition.

The application discloses a lossless compression storage method and system based on heterogeneous data in a Xinhua environment, first extracts the file type and structure feature mapping relationship by scanning the target file, classifies and groups, and then carries out preliminary lossless compression, further adopts a sliding window matching and sequence matching algorithm to optimize the compressed data for the redundant mode, then generates a metadata structure containing a check code and compression parameters, encapsulates into a data packet supporting cross-platform transmission, finally transmits to the target storage device through a secure write protocol, and verifies the data consistency and recovery path feasibility by using the check code. The application significantly improves the data compression efficiency, ensures the compatibility of cross-platform transmission and the integrity of storage data, and provides efficient and reliable technical support for complex electronic file processing.

The invention relates to the technical field of spacecraft satellite-ground communication, and provides a low-earth-orbit satellite communication time delay optimization method and system aiming at the defects that a traditional low-earth-orbit satellite communication satellite system and a satellite communication frame have high time delay and poor real-time communication function. The core of the invention comprises three parts: firstly, an efficient receiving and transmitting end coding and decoding technology: a transmitting end adopts a subblock coding technology of a Turbo code; and a receiving end uses an MAX-Log-MAP algorithm to carry out rapid iterative decoding on the word block code of the Turbo code. And secondly, compressing the user data: carrying out lossless compression on the user data and a satellite communication frame header by using Huffman coding, reducing the transmission data volume, improving the data processing speed, and thus reducing the processing time delay. And finally, simplifying the frame header design: optimizing the frame header of the communication frame so as to reduce the total length of each frame. The method is particularly suitable for communication satellites (the orbit height is less than or equal to 1500km) in a low earth orbit.

This invention provides a lossless compression and decoding method for depth images. Lossless compression includes: dividing sequential data into multiple independent image blocks according to predetermined partitioning rules; selecting a corresponding thread model based on the characteristics of the depth image; calling a thread based on the thread model and using a reversible encoding algorithm to independently encode each image block; wherein, at the start of encoding for each image block, the encoder state is reset so that the encoding of each image block does not depend on the data of the preceding image block; generating metadata containing position index and compressed data length information and assembling it with the encoded image block for output. Lossless decoding includes: reading the metadata and decoding at least one image block based on the metadata. Through the above methods, parallel encoding and decoding are achieved; precise positioning via physical offsets and random access and on-demand region decoding are supported, improving the processing response of high frame rate depth streams under low computing power environments while ensuring lossless restoration.

The invention discloses a radar data compression and transmission method based on wavelet lifting-DCT (Discrete Cosine Transformation)-mixed entropy coding. The method comprises the following steps: processing data into a two-dimensional matrix format; the network condition is monitored in real time, and the network is selected according to the availability and bandwidth characteristics of the network; setting a dynamic compression ratio according to a network environment; performing biorthogonal wavelet transformation on the two-dimensional data in the row direction, performing biorthogonal wavelet transformation on the transformed result in the column direction, and removing redundant information; a block-shaped DCT method is adopted, DCT is further conducted on the low-frequency part and the high-frequency part after wavelet transformation, coefficients are effectively concentrated to the upper left corner of a matrix, energy distribution is concentrated, the compression efficiency is improved, and meanwhile the block size is dynamically determined by monitoring the network state according to the parameters and a dynamic compression ratio setting rule; a mixed entropy coding method is adopted, and lossless compression is carried out by using data statistical characteristics; and transmitting the compressed data.

The application discloses a data hierarchical compression storage method and an energy storage management system, device and storage medium, and comprises the following steps: acquiring operation data of a cascaded energy storage system; classifying the operation data into first data, second data and third data according to the reading frequency and the importance of the data; compressing the second data by using a second lossless compression step to form second storage data, wherein the compression rate of the first lossless compression step is lower than that of the second lossless compression step; screening the third data by using a decision tree screening step to form third storage data, wherein the decision tree screening step is provided with a screening condition, part of the content in the third data is deleted based on the screening condition, and the remaining content in the third data forms the third storage data; and storing the first storage data, the second storage data and the third storage data in a local storage, so that the classified storage improves the flexibility of storage reading, reduces the storage load of the storage and improves the reliability of data storage.

The invention relates to the technical field of data compression, in particular to a real-time lossless compression method for linear scanning panoramic infrared image data. The method comprises the following steps: receiving current column original pixel data output by a linear column scanning infrared sensor; taking the original pixel data of the current column and the coded pixel data of the adjacent column in one frame as reference columns, and performing backward inter-column difference prediction to obtain predicted residual data of the current column; mapping the predicted residual data into a symbol value; carrying out entropy coding on the symbol value by using a static Huffman coding table which is pre-generated and solidified based on the statistical characteristics of the linear scanning infrared image, and outputting a compressed code stream; according to the scheme, the technical problem that high real-time performance and high compression ratio are difficult to consider when real-time lossless compression is carried out on linear scanning panoramic infrared image data in the prior art is solved.

The application discloses a large language model weight compression method, an inference operation method, a system and a medium, and belongs to the technical field of large language model weight compression and inference operation optimization. The method comprises the following steps: acquiring a weight matrix of a large language model and constructing an exponential high-frequency window and a benchmark value, classifying and encoding each weight data into a fixed-length code word and splitting the fixed-length code word into an independent bitmap, synchronously generating a compact value stream and a rollback value stream, and packing the compact value stream and the rollback value stream into weight compression data; loading the weight compression data, generating a state mask and a channel prefix mask in a register based on the bitmap, calculating a reading offset by using a population count instruction, reading a compressed data segment from a corresponding data stream, reconstructing weight data in the register in combination with the code word, and finally directly inputting the weight data into a matrix multiplication unit for operation; therefore, by implementing the application, the problem of efficiency reduction caused by control flow divergence and redundant memory access in GPU inference of existing variable-length encoding can be solved, and the model inference efficiency under lossless compression can be improved.

The invention discloses a dot matrix font library compression method and device, electronic equipment and a storage medium. The method comprises the following steps: in response to triggering of a dot matrix font library compression event, determining zero data in a to-be-compressed dot matrix font library; wherein the zero data is dot matrix data which is not depicted; deleting each piece of zero data from the dot matrix font library to be compressed, and respectively determining an original initial address of each piece of zero data and an accumulated zero removal length before each piece of zero data; generating an initial zero removal table based on the original initial address and the accumulated zero removal length corresponding to each piece of zero data; and generating a target dot matrix font library based on the initial zero removal table and the to-be-compressed dot matrix font library after the zero data is deleted. Through the technical scheme provided by the embodiment of the invention, lossless compression can be performed on the dot matrix font library, and the storage space occupied by the dot matrix font library is effectively reduced.

The application discloses a kind of semi-automatic bootstrap medical image sequence marking method, comprising the following steps: facing single frame or multiple frames DICOM and its derived sequence, frame analysis and export lossless image sequence, establish sequence index to support hierarchical batch access, sampling and cross-frame operation, estimate imaging window (sound window) and crop by interframe pixel variation statistics, support manual frame selection, reuse history crop frame and skip abnormal sequence, when marking, based on prompt point set and neighborhood brightness distribution adaptive threshold determination, connected extraction generates effusion target mask;Magnetic tracing is provided to highlight boundary and bone surface boundary is generated along texture fitting, optionally reuse last frame prompt point / mask or optical flow propagation to subsequent frame, and support cross-frame interpolation, and can be integrated segmentation model automatic marking, correction and incremental training to form closed loop;Mask is saved with file tail additional lossless compression section, supports cover update, does not affect general reading and can be restored.

The application discloses an end-side large model inference acceleration method and equipment based on lossless compression. In the offline preprocessing stage, firstly, the large model weight parameters are decomposed by bits, and the exponential bit data and the decimal bit data are separated; then, the exponential bit data is losslessly compressed, and the compressed exponential bit result and the uncompressed decimal bit data are stored in a solid state disk. In the online inference stage, the GPU memory allocation budget is sensed in real time, and the tensor cache strategy is dynamically adjusted accordingly; when the parameter loading is performed, the scheduler optimally schedules the calculation and I / O workflow related to the tensor recovery according to the current cache state. Under the premise of ensuring zero precision loss of the end-side inference of the large model, the first word delay and the word delay of the model inference are significantly reduced, and the overall throughput in the batch processing scene is greatly improved.

The invention provides a remote sensing image lossless compression method based on overlapped biorthogonal transformation and Transform, and belongs to the technical field of remote sensing image lossless compression coding, and the method comprises the steps: converting a remote sensing image pixel to a frequency domain through overlapped biorthogonal transformation, and obtaining a direct current (DC), a low frequency (LP) and a high frequency coefficient (HP); partitioning the three coefficients, recombining the three coefficients into coefficient tensors, and performing probability estimation on the coefficients by using a probability modeling network to obtain a coefficient estimation probability; and coding according to the estimation probability of the coefficient to obtain a compressed code stream, thereby realizing the lossless compression task of the remote sensing image. According to the method, an overlapped biorthogonal transformation method is utilized, the spatial redundant information of an original image is reduced, three kinds of frequency domain coefficients in concentrated distribution are obtained, accurate probability estimation is carried out on the frequency domain coefficients through strong feature extraction and parameter fitting capabilities of Transformer, efficient lossless compression of the remote sensing image is realized, and the compression efficiency of the remote sensing image is improved. The performance is obviously superior to that of a traditional lossless compression method.

The invention discloses an ultrahigh-resolution image semantic segmentation algorithm based on semantic feature enhancement, and belongs to the field of computer vision. The method is characterized by comprising the following steps: step 1, inputting an original image I0; 2, preprocessing the original image I0 to obtain an image I1 and an image P1; 3, performing double-branch feature extraction on the image I1 and the image P1; step 4, carrying out feature fusion on the feature map obtained through the double-branch feature extraction in the step 3 to obtain a final feature map; and 5, generating a final semantic segmentation mask with the same resolution as the original image I0 through the final feature map obtained in the step 4. According to the ultrahigh-resolution image semantic segmentation algorithm based on semantic feature enhancement, lossless compression and information enhancement are realized for the original image by using the advantages of wavelet transform in the aspects of time-frequency analysis and multi-resolution representation of the signal, and dual-branch feature extraction is adopted, so that the image segmentation accuracy is improved. And the contradiction among the segmentation precision, the memory consumption and the reasoning speed is balanced.

The invention relates to the technical field of image compression and artificial intelligence, and discloses a full-view pathological image self-adaptive differential compression method based on AI semantic importance and application of the full-view pathological image self-adaptive differential compression method. In order to solve the problems of high storage cost and loading lagging caused by a huge full-view pathological image file, the method comprises the following steps: acquiring a low-resolution thumbnail of the full-view pathological image file, inputting a pre-trained semantic evaluation model, and generating a two-dimensional probability mask comprising an extremely low importance level, a medium importance level and a high importance level; mapping the coordinates of the image to a high-resolution image and dividing the image into grid blocks; color mean values are extracted from the blocks with extremely low importance to perform bypass replacement, lossy and lossless compression is performed on the blocks with medium importance and the blocks with high importance respectively, and smooth transition areas are generated at junctions; and finally generating a compressed file embedded with the index dictionary. According to the invention, the storage occupation and transmission delay of the pathological data are effectively reduced, and the diagnosis fidelity of the core focus area is guaranteed.

This invention discloses a wireless low-latency video receiver line block compression and linked recovery system, belonging to the field of wireless video transmission and display technology. After decoding at the receiver, the system performs line block-level visual lossless compression and storage of the image, replacing traditional whole-frame storage. The display side reads the compressed line blocks on demand, decompresses them, and stores them in a small-capacity display buffer. Combining innovative technologies such as adaptive line block compression strategies, intelligent prefetching mechanisms, multi-level error detection, dynamic buffer management, and frame rate linkage control, the system significantly reduces display latency and improves its recovery capability and display smoothness under wireless channel interference. While ensuring visual losslessness, it can reduce display latency by more than 50% and storage bandwidth requirements by more than 65%, making it suitable for latency-sensitive applications such as wireless projection, remote control, and video conferencing.

This invention relates to the field of data encoding and compression technology, specifically to a lossless compression format for motion data used in artificial intelligence training, along with its encoding and decoding method and system. It includes the following steps: acquiring raw FIT data files collected by a motion device; parsing the message header and data body using a FIT parser to extract device information, motion session metadata, and time-series records containing timestamps, GPS coordinates, and multi-channel sensor data to generate a structured internal data object; establishing a HAD compression container and sequentially writing the file header and compression metadata area into the HAD compression container; and compressing the timestamps based on the structured internal data object using sampling interval detection. This invention significantly reduces structural redundancy in temporal, spatial, and multi-channel physiological parameters by introducing mechanisms such as timestamp frame encoding, GPS coordinate differential and run-length compression, and sensor data stationary segment detection and hybrid encoding.

This invention provides a 3D visualization method for industrial equipment based on key feature recognition, belonging to the field of industrial equipment digitization technology. The method achieves accurate lightweighting and efficient visualization of industrial equipment 3D models through a five-step process: constructing a standardized knowledge base for functional components of power distribution equipment, identifying key regions based on a knowledge-enhanced graph convolutional neural network, hierarchical lightweight reduction of polygons, ASTC texture hierarchical compression, and a line-of-sight adaptive LOD mechanism. This invention uses a hierarchical polygon reduction technique that preserves key features, dividing the power distribution equipment into key and non-key feature areas, and selectively allocating polygon reduction rates of 20% and 80%. Combined with an ASTC near-lossless compression strategy for key areas, the recognition accuracy of key information such as circuit breaker opening / closing indicators and busbar T-type connection structures remains above 95%. Simultaneously, a topology protection algorithm ensures the integrity of the overall model structure and connection relationships, balancing model simplification with industrial practicality.

An encrypted and untraceable communication between a first and second specially enabled devices that subscribe to a network of such devices with the first device lossless compressing and encrypting a binary input file (IFDS) using an encryption key that is hardcoded in the two devices only, with both encryption and compression processing the IFDS content in term of universal sets of binary constructs based on which the IFDS is uniquely partitioned and transformed, where the encryption and compression is performed over multiple cycles with the order of such cycles being specific to the two devices and hardcoded in the two devices as part of the encryption key, and where the untraceability is achieved by first device sending the encrypted and compressed file to the second device and to one up to all devices in the network and by creating a simulated network traffic.

Described herein is a data processing system having a multisample antialiasing compressor coupled to a texture unit and shader execution array. In one embodiment, the data processing system includes a memory device to store a multisample render target, the multisample render target to store color data for a set of sample locations of each pixel in a set of pixels; and general-purpose graphics processor comprising a multisample antialiasing compressor to apply multisample antialiasing compression to color data generated for the set of sample locations of a first pixel in the set of pixels and a multisample render cache to store color data generated for the set of sample locations of the first pixel in the set of pixels, wherein color data evicted from the multisample render cache is to be stored to the multisample render target.

Methods and apparatus of decoding for transform coefficients of residual data are disclosed. According to one method, one or more high-level syntax elements indicating whether to enable a controlling scheme are parsed. In response to said one or more high-level syntax elements indicating the controlling scheme being enabled: a value for a target syntax flag related to representation of the transform coefficients is inferred as an inferred value for the target syntax flag when a target probability for a selected context variable associated with the target syntax flag corresponds to a specified high / low probability range. According to another method, one or more high-level syntax elements indicating whether the current bitstream represents a coded input video data by lossless compression are parsed. According to yet another method, a pre-defined parameter setting for lossless compression is determined, wherein all coding tools that cause coding artefacts are disabled.

State of the art techniques for reduced data transmission at edge devices while using lossless compression mostly rely on CNNs that are energy consuming networks. However, for edge devices such as LEO satellites the method and system discloses a unique combination of SNN running on a neuromorphic platform for low-power & low-latency. Relevant data of high-resolution images is filtered, and redundant data is discarded. The filtered data is then compressed using but packing based lossless compression before transmitting to a central station for further processing. At the central station a distributed Diagonal Parallel Fast Reconstruction (DDPFR) is applied to reconstruct the image. The system reduces data sent to the central station and reduces battery consumption of satellite for data transmission. SNN running on neuromorphic processor consumes up to 100 to 1000× less power compared to similar Artificial neural network running on GPU.

This invention discloses a data compression method based on data difference features. The method includes a compression code table generation stage and a data compression stage. During code table generation, the data is first segmented, retaining the first data element of each segment. Subsequent data elements are then subtracted from their preceding elements, and the difference features are statistically analyzed to generate the compression code table. During data compression, the same steps are used to obtain the data difference distribution. The data is then encoded using the compression code table, reducing the total data length, and finally, the compressed data segment is output. This invention generates a code table based on the difference distribution features between data elements, performs lossless data compression, and, considering the characteristics of hardware devices, performs segmented data compression, allowing for parallel operation, thereby improving compression and transmission efficiency and saving transmission bandwidth.

The present application relates to the technical field of image compression, and particularly relates to a mass certificate image data processing and analyzing method, which obtains a portrait area in a gray image corresponding to any certificate image, and obtains a plurality of feature points in the portrait area; performs superpixel segmentation on the portrait area to obtain a plurality of superpixel blocks; obtains an attention degree of any superpixel block according to energy of a gray level co-occurrence matrix of a center point of the superpixel block in at least two preset directions, and in combination with an importance degree of each feature point contained; corrects the attention degree of any superpixel block to an attention degree correction value; marks a lossy compression area and a lossless compression area of the gray image according to the attention degree correction value of each superpixel block to obtain a marked binary image; and compresses the marked binary image of the mass certificate image to obtain a corresponding compression result, so that the data amount of a single certificate image is reduced, the data amount of the mass certificate is reduced, and storage space is saved.

The present application belongs to the technical field of image processing, and particularly relates to a TEDS image lossless compression method and system based on adaptive difference, which comprises the following steps: obtaining original gray data stream collected by TEDS trackside equipment, and establishing a space-time mapping reference; calculating local space-time activity based on space-time neighborhood pixel difference, and obtaining response lag by comparing with historical reference data; determining adaptive difference gain coefficient according to the response lag, and obtaining a final prediction value by weighted summation of the basic prediction value and the pixel at the previous spatial position using the coefficient; and calculating prediction residual and performing entropy coding. By introducing a physical response lag model, the present application dynamically adjusts the prediction gain, improves the prediction problem of non-stationary signals in the skin flat area and the mechanical assembly complex area of the EMU image, suppresses the prediction overshoot, reduces the residual dynamic range, and improves the efficiency of lossless compression.