237 results about "Lossy compression" patented technology

The invention discloses a data compression transmission method and system applied to a ferry inspection image, and the method comprises the steps: collecting and obtaining the ferry inspection image in real time, recognizing a key inspection target region in the image, and carrying out the segmentation and partitioning of the image; compressing the key inspection target area based on lossless compression coding; the quantization step size is dynamically adjusted by comparing statistical variances of background pixels between continuous frames, and lossy compression coding is carried out on a background area; based on the boundary distance between the key inspection target area and the background area, adaptive compression coding is carried out on the transition area; constructing a hierarchical data packet; and constructing a data transmission optimization model, dynamically allocating data transmission links, and obtaining a transmission scheme with the highest total transmission. The method has the advantages that efficient data compression transmission is realized by accurately segmenting the image area and adopting a lossless, lossy and adaptive compression technology, the overall transmission efficiency is improved through intelligent transmission optimization, and the definition and real-time performance of the inspection image are ensured.

A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

A computer system for upsampling decompressed audio data after lossy compression using specialized neural network techniques. The system processes compressed audio channels through an audio pre-processor that extracts spectral information, detects speech activity, segments audio, and normalizes input levels. A trained deep learning algorithm with multi-channel transformers using channel-wise and self-attention mechanisms recovers information lost during compression. The system further enhances audio quality through a time-frequency domain transformer applying Fourier transforms and Mel-scale frequency processing, while a perceptual quality assessor employing psychoacoustic models evaluates the output. This specialized audio processing approach significantly improves reconstructed audio quality by leveraging correlations between audio channels, addressing both spectral and temporal features, and optimizing for human perception characteristics, resulting in higher fidelity audio reproduction from compressed formats.

The invention is suitable for the technical field of power transmission line alarm, and provides a power transmission line alarm method based on fault data flow dynamic analysis and lightweight compression, and the method comprises the steps: a terminal collects line state data in real time through a multi-mode sensor disposed on a power transmission line; the terminal carries out lightweight compression processing on the line state data, lossless compression is carried out on steady state data by using improved Huffman coding, and lossy compression is carried out on transient fault data by using combination of wavelet transform and sparse coding; the terminal uploads the compressed data to the cloud according to a preset priority scheduling rule; the cloud end automatically identifies a data format and decompresses the data format through a protocol non-inductive analysis engine, and reconstructs a transient waveform; performing space-time correlation analysis on the analyzed line data, meteorological data and a historical fault library, and calculating a fault confidence coefficient; and according to the fault diagnosis result, generating a grading alarm instruction, and transmitting the grading alarm instruction to the terminal for visualization, thereby improving the power grid fault response speed and the operation and maintenance intelligent level.

Systems and methods are provided for obtaining a media, the media including an image, audio, video, or combination thereof. An input may be received regarding one or more features or frames of the media to be maintained in or removed from the media. One or more criteria of a lossy compression technique may be inferred based on the received input, using a machine learning model, based on the received input. The inferred criteria of the lossy compression technique may be applied to the media.

A method for streaming dynamically changing user interface data from a server to a client, including resizing user interface data in accordance with a client display, repeatedly transmitting in the form of IP packets, the user interface data in a lossy compression format, for each transmission, if an acknowledgement is received that all packets have been received, then increase the number of IP packets in the next transmission, and if the increased number of IP packets is sufficient to transmit the user interface data in a lossless compression format, then retransmit previously transmitted user interface data in the lossless compression format, including partitioning previously transmitted user interface data into display strips, and for each strip, if the user interface data is unchanged, transmit the strip in the lossless compression format, and if the user interface data has changed, return to the repeatedly transmitting for the changed user interface data.

A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.

The invention discloses an apparatus and method for converting compressed geometries into accelerated data structures. An apparatus and method for transforming lossy compressed geometries into a bounding volume hierarchy. For example, one embodiment of a method includes constructing a bounding volume hierarchy (BVH) based on a compressed hierarchy (LOD) structure formed from iteratively merged geometric primitive cluster pairs, where constructing the BVH includes traversing the compressed hierarchy (LOD) structure, to select a subset of the cluster on one or more levels of a compressed hierarchy (LOD) structure based on the current cone of view; decompressing each cluster and constructing a per-cluster BVH on the primitive of each cluster, wherein each per-cluster BVH comprises a per-cluster BVH root node; and fusing the per-cluster BVH root nodes to form a BVH, the BVH being used to efficiently ray trace all decompressed geometric primitives in the scene.

A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.

The invention discloses a holographic image information processing system, which comprises an edge calculation and data preprocessing module, a data compression and coding module, a holographic image information processing module, a holographic image information processing module, a holographic image information processing module, a holographic image information processing module, a holographic image information processing module and a holographic image information processing module, wherein the edge calculation and data preprocessing module is responsible for acquiring original holographic image data from holographic image acquisition equipment or a storage system; according to the holographic image compression method, lossless compression or lossy compression is decided to be adopted by judging image quality requirements, image data needing to keep high quality of a holographic image are compressed by adopting a Huffman coding lossless compression algorithm, and image data receiving quality loss are compressed by adopting a JPEG lossy compression algorithm. The system can intelligently select a lossless or lossy compression algorithm according to the image quality requirement, effectively reduces the data volume of the holographic image, and guarantees the image quality. The parallel transmission and streaming media processing module can transmit a plurality of data blocks at the same time by using a multi-path parallel transmission technology, so that the overall transmission time is remarkably shortened.

The invention provides a structured memory data processing method and system oriented to long time sequence interaction, and is applied to the technical field of natural language processing and artificial intelligence memory modeling. The method comprises the following steps: acquiring a real-time interaction data stream, partitioning the real-time interaction data stream into dialogue data blocks, loading a hierarchical attribute mode and a previous time step attribute tree instance, inputting serialized texts of the hierarchical attribute mode and the previous time step attribute tree instance into a generative model, generating a writing, rewriting, deleting or null operation instruction aiming at a leaf node path, analyzing, updating, generating a current attribute tree instance, and storing the current attribute tree instance; by means of the scheme, lossy compression and structured evolution of the infinite long dialogue stream can be achieved, memory forgetting is relieved on the premise that a context window is not expanded, and long-time-sequence information retrieval precision and storage efficiency are improved.

A method of encoding data includes determining, by an encoder, a first data instance by corrupting the data with Gaussian noise. The method also includes determining, by the encoder, information representative of one or more conditional distributions. The method additionally includes determining, by the encoder, an index of a corrupted data instance of the sequence of progressively less corrupted data instances. The index corresponds with a conditional distribution of the one or more conditional distributions which causes the corrupted data instance to have a desired bit-rate. The method further includes transmitting, from the encoder to a decoder, the first data instance and the information representative of the one or more conditional distributions to enable the decoder to recover the corrupted data instance having the desired bit-rate and use the corrupted data instance to generate output data representative of the data.

The invention relates to a wave band self-adaptive hyperspectral image compression method based on semantic guidance. According to the method, a compression task and a classification task are unified under the same framework, and a natural relation between a spectral curve and a semantic category is utilized to apply category constraint to a spectral structure, guide generation of spectral information of a reconstructed image, and relieve adverse effects of spectral structure mutation on a compression process and subsequent hyperspectral image analysis and interpretation. According to the method, semantic information of ground features under a high compression ratio is effectively reserved, and the adverse effect of lossy compression on downstream task analysis and interpretation is relieved while bandwidth and storage resources are saved. Besides, the compression framework can process the hyperspectral data of any wave band, and does not need to train an independent model according to the hyperspectral images of different wave bands, so that the storage cost of the compression model is reduced.

An image processing method and a data set collection method is disclosed. The image processing method includes: making a high-resolution high-definition image interact with a calibrated blur kernel to obtain a blurred image; injecting calibrated noise to form a first noise image; degrading, by using a downsampling method, the first noise image into a low-resolution image; performing lossy compression on the low-resolution image to obtain a compressed image; adding into the compressed image random noise points that simulate a transmission process, to form a second noise image; and performing lossy compression on the second noise image to obtain a low-definition image, where the high-definition image is an image that is formed by means of photographing an organ in a body using a capsule endoscope, and the calibrated blur kernel and calibrated noise are results that are obtained by means of fitting a photographing process of the high-definition image.

The invention designs a high-magnification micro-loss satellite image data lightweight method, which comprises the following steps of: firstly, acquiring a region of interest by using an information extraction model according to different task requirements, and extracting the region of interest and a background region; in the aspect of an interested selection strategy, an image is divided into a key area, an associated area and an auxiliary area, lossless / micro-lossy compression is adopted for the key area and the associated area, lossy compression is adopted for the auxiliary area, and then the compressed image is reconstructed. Compared with the prior art, the method has the advantages that high-magnification light weight of the satellite image data is realized, and under the condition that the light magnification is as high as 20 times, the volume of the satellite image data is greatly reduced, and the transmission, visualization and re-editing efficiency of the satellite image data is greatly improved.

A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.

The invention relates to deep learning large-scale model parallel training communication optimization, in particular to a large-scale parallel communication optimization method based on lossy compression, and the method comprises the steps: dividing original data into N * N pieces of block data; each process receives the block data from the previous process in a non-blocking manner, compresses the block data received in the previous round of the process based on an SZX algorithm, sends the compressed block data to the next process in a non-blocking manner, decompresses the received block data and performs protocol operation until each process holds a protocol sub-block result; each process is in parallel communication in an annular mode until each process holds a protocol sub-block result after compression of all the processes; and each process starts to decompress all received compressed protocol sub-block results and stores data. According to the method, the communication overhead in large model tensor parallel training is reduced, mutual masking of sending and receiving as well as compression and decompression time overhead is realized, and the time cost is saved.

The invention provides an electroencephalogram data transmission method combining lossless and lossy compression, belongs to the field of electroencephalogram data transmission and compression processing, and is used for solving the problems of low transmission efficiency of pure lossless compression and easy loss of key diagnosis information of pure lossy compression in related technologies. According to the method, multi-dimensional features of electroencephalogram data are extracted, a key area and a non-key area are divided, lossless compression and lossy compression processing are adopted respectively, the area division proportion is dynamically adjusted in combination with transmission bandwidth, signal features and application scenes, and corresponding data are transmitted through dual protocols. The transmission efficiency can be improved while the clinical diagnosis marker and the core characteristics are reserved, and the scene adaptability and the transmission reliability are achieved at the same time.

The invention discloses an ocean field data lossy compression method considering space-time heterogeneity. The method comprises the following steps: (1) dividing ocean three-dimensional space-time field data into n field data blocks which are uniformly distributed in space in space; (2) carrying out dimensionality reduction and characteristic modal decomposition on the data blocks by considering spatial-temporal heterogeneity, and organizing and representing a decomposition result in a form of a plurality of spatial-temporal characteristic levels; (3) setting a maximum compression error, determining the number of main hierarchies, namely hierarchies containing main change information of an original data block, performing main hierarchies extraction on hierarchies representation, storing main hierarchies features and a corresponding time sequence in the form of objects, recording block information of data and a mean value of all space lattice points in time, and performing compression on the data according to the mean value; and data compression is realized. According to the method, data block information, selected main spatial feature levels and corresponding time change sequences are organized into a compression result file, and dimensionality reduction compression of high-dimensional data is achieved.

One example method includes at a source, at a source, performing a symbolic aggregation process on a series of raw data generated and / or collected by the source, to create a series of symbols, inputting, by the source, the series of raw data and the series of symbols to a lossy compression algorithm operating at the source, running, at the source, the lossy compression algorithm to obtain a series of raw values, and a sparse series of raw values, and transmitting, by the source to a target, the series of raw values, and the sparse series of raw values.

A system and method for enhancing lossy compressed data. The system receives a compressed data stream, decompresses it, and enhances the decompressed data using adaptive neural network models. Key features include data characteristic analysis, dynamic model selection from multiple specialized neural networks, and quality estimation with feedback-driven optimization. The system adapts to various data types and compression levels, recovering lost information without detailed knowledge of the compression process. It implements online learning for continuous improvement and includes security measures to ensure data integrity. The method is applicable to diverse data types, including financial time-series, images, and audio. By combining efficient decompression with advanced neural upsampling, the system achieves superior reconstruction of lossy compressed data, enabling improved data transmission, storage, and analysis in bandwidth-constrained or storage-limited environments while maintaining data quality and security.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a control message that indicates one or more parameters for lossy compression of a feedback payload. The one or more parameters may indicate at least a compression ratio between an original feedback payload size and a compressed feedback payload size. The one or more parameters may also indicate a bundle size, a quantity of bundles, a quantity of most likely outcomes to distinguish, a quantity of negative acknowledgments (NACKs) to be distinguished, or a combination thereof. The UE may monitor for one or more downlink transmissions associated with the feedback payload and transmit the feedback payload based on the one or more downlink transmissions. The feedback payload may be compressed from the original feedback payload size to the compressed feedback payload size in accordance with the one or more parameters.

A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.

A computer-implemented method that provides watermark-based image reconstruction to compensate for lossy encoding schemes. The method can generate a difference image describing the data loss associated with encoding an image using a lossy encoding scheme. The difference image can be encoded as a message and embedded in the encoded image using a watermark and later extracted from the encoded image. The difference image can be added to the encoded image to reconstruct the original image. As an example, an input image encoded using a lossy JPEG compression scheme can be embedded with the lost data and later reconstructed, using the embedded data, to a fidelity level that is identical or substantially similar to the original.

The invention discloses an image lossless compression method and system based on a pre-trained large language model, and the method comprises the steps: carrying out the BPG compression of a to-be-compressed image, obtaining a lossy image, and storing the lossy image in a bit stream; obtaining a residual error; with a lossy image as a condition, probability distribution of residual errors is obtained through a large language model; carrying out compression coding on the residual error based on probability distribution, and storing the residual error in a bit stream; during decoding, extracting a lossy image part from the bit stream and carrying out BPG decoding to obtain a lossy image; with a lossy image as a condition, probability distribution of residual errors is obtained through a large language model; decoding and restoring the residual error by utilizing the residual error part in the bit stream and the probability distribution of the residual error; and restoring the image based on the lossy image and the restoration residual error. According to the method, a lossy compression prediction residual distribution framework is combined with a large language model, and end-to-end joint optimization is carried out. And the purpose of efficiently and losslessly compressing the image is achieved by utilizing the strong autoregression capability of the large language model.

A device for decoding video data includes an integrated circuit (IC) comprising a video decoder, and a memory that is external to the IC and coupled to the IC. The video decoder is configured to in a first mode, decode a first frame based on a first reference frame stored in the memory, and in a second mode, decode a second frame based on a second lossy reference frame, wherein the second lossy reference frame is generated based on decompression of a lossy compressed reference frame.

The invention discloses a power grid real-time data elastic capacity expansion system based on mass streaming data, relates to the technical field of power grid real-time data storage, and aims to solve the problem that a storage environment is not safe due to the fact that more intelligent elastic capacity expansion storage is not adopted when existing power grid data is stored. Through a real-time capacity monitoring function, the system can know the capacity condition of the storage node in time, so that an intelligent storage scheduling decision is made, when the capacity of the storage node is insufficient, a capacity expansion process can be automatically triggered, the need of manual intervention is reduced, and through dynamic capacity expansion and load balancing, storage resources can be more effectively utilized, and the capacity expansion efficiency is improved. By monitoring and analyzing the power grid data in real time, abnormal conditions in the power grid can be found in time, corresponding measures are taken for prevention and processing, and the storage space and transmission bandwidth requirements of the data can be effectively reduced by adopting a lossy compression mode.

The invention relates to the field of data compression, in particular to efficient compression of DNS traffic logs. In the current mainstream technology, a gzip algorithm is generally adopted to directly compress a DNS flow log file. Due to the fact that the compression effect in the prior art is limited, in order to save storage space more effectively, a compression technology based on timestamp difference, TOP domain name coding, DNS address coding and domain name distribution recombination is adopted to process DNS traffic logs. For domain name compression, common TOP domain name codes are used for replacement. For DNS address compression, full-amount DNS address codes are adopted for replacement, and the data volume is further reduced. For timestamp compression, a current time date is converted into a timestamp, and then the timestamp is replaced by a time difference value. In addition, aiming at the problem that the compression effect of a large-section DNS traffic log is poor, the invention provides a new method: compression is carried out by using a domain name distribution recombination technology. Therefore, by improving the similarity of the DNS traffic logs, the gzip compression effect is further improved.

Lossy methods and hardware for compressing data and the corresponding decompression methods and hardware are described. The lossy compression method comprises dividing a block of pixels into a number of sub-blocks and then analysing, for each sub-block, and selecting one of a candidate set of lossy compression modes. The analysis may, for example, be based on the alpha values for the pixels in the sub-block. In various examples, the candidate set of lossy compression modes comprises at least one mode that uses a fixed alpha channel value for all pixels in the sub-block and one or more modes that encode a variable alpha channel value.

The invention discloses a smart factory large-scale data storage and analysis method, and relates to the technical field of industrial Internet of Things, and the method comprises the steps: collecting three-dimensional physical field data of equipment, and carrying out the timestamp alignment and numerical range filtering to generate a standardized data stream; dynamically adjusting a compression level based on the structured data stream, calculating vibration waveform data lossy compression and heat flow field feature vector lossless compression through physical constraint residual errors, and generating a compressed data block; according to the address mapping table, constructing a high-dimensional topological space, extracting bar code life cycle and hole forming position features, and generating a topological feature vector; and performing deviation degree comparison on the topological feature vector and a persistent graph in a historical working condition library, and triggering equipment parameter adjustment according to a preset deviation degree threshold value. Differential compression of vibration waveform data and heat flow field characteristics is achieved through physical constraint residual calculation, key physical field information is reserved while the data size is reduced, and the compression efficiency is improved.