183 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.

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.

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 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.

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 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.

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.

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.

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.

Deep learning data compression using multiple hardware accelerator architectures is provided herein. A system includes a computing device and first and second hardware accelerators coupled thereto. The first and second hardware accelerators may be of different types, such as a tensor streaming processor and a field programmable gate array. The first and second hardware accelerators may be directly connected to one another, such as by a chip-to-chip connection. The first and second accelerators may implement different stages of a data pipeline, such as lossless and lossy compression stages of a learned image compression.

This invention provides an end-to-end voice encryption method, apparatus, and Bluetooth headset suitable for multi-hop lossy channels. The method includes front-end noise reduction, framing, and segmentation of the acquired voice at the transmitting end. Based on the session mode, a group public key or a point-to-point private key is automatically selected as the master key from a pre-configured key set. Then, multiple time-domain segments within each frame are scrambled, subjected to segment-by-segment Fast Fourier Transform, and frequency-domain encryption based on subkeys, according to the master key. The encrypted voice is then generated through inverse transformation and overlapping weighted smoothing concatenation, and transmitted through a multi-hop voice communication channel containing multi-level lossy encoding and decoding. At the receiving end, the reverse process is performed: framing and segmentation, transformation, inverse frequency-domain encryption, and inverse time-domain scrambling. Combined with U-shaped neural network noise reduction at both the front-end and back-end, the voice is restored to intelligible speech. This method enables flexible key management and high-quality secure voice transmission in scenarios where group calls and point-to-point private calls coexist and undergo multiple lossy compression operations.

The embodiment of the application discloses a lossy compression method, a decompression method and equipment for time sequence floating point data, which are used for improving the data compression rate and the decompression speed of the time sequence floating point data. The method comprises the following steps: acquiring a floating point array, the floating point array comprising a plurality of floating point data arranged in time sequence; sequentially determining other floating point data in the floating point array as target floating point data, and determining a previous floating point data of the target floating point data from the floating point array; determining a difference between the target floating point data and the previous floating point data as a difference data of the target floating point data; performing quantization processing on the difference data based on a preset error boundary to obtain quantized difference data corresponding to the target floating point data, wherein a difference between lossy data corresponding to the target floating point data and the target floating point data is not greater than the preset error boundary, and the lossy data corresponding to the target floating point data is obtained by performing inverse quantization processing on the quantized difference data corresponding to the target floating point data; and performing compression processing on each quantized difference data.

A data storage system (90) that stores data that is lossy compressed includes a lossy compression device (9). The lossy compression device (9) includes a smoothness decision unit (18) that decides smoothness according to the rarity of an event indicated by subject data, as subject smoothness, and a data smoothing unit (22) that generates smoothed subject data by smoothing the subject data with the subject smoothness.

A system and method for complex-valued radar image compression integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of convolutional layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The convolutional layers extract multi-dimensional features from the complex-valued radar image, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving image quality. The model's outputs enable effective complex-valued radar image reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

Image processing using various video and still flows is described. The resolution and bit depth at each stage of the image processing are described. In some examples, image scalers are used to resize image resolution. In some examples, a warp engine is used to distort per frame images to apply image stabilization, zoom, or a user digital lens. An image processing pipeline includes a double data rate (DDR) memory buffer that supports lossy compression with a constant 50% compression. In some examples, the image processing pipeline includes a DDR memory buffer that is uncompressed.

The application discloses a kind of based on information redundancy's call chain data compression method and system, method includes: for the call chain data collected, similar data is extracted using KMeans algorithm, the data point in the call chain data is grouped, and each cluster group is obtained;Wherein, the data similarity in the same cluster group is high, and the data similarity between different cluster groups is low;Using the offline tail sampling strategy based on hierarchical clustering, the cluster group is sampled and handled, and a structured file is output;The structured file is losslessly compressed using a lossless compression method, and the compression of the call chain data is completed.The application is based on the characteristics of call chain data, combined with the sampling idea of lossy compression, greatly reduces the size and storage burden of the compressed file on the basis of maintaining data analysis value, improves efficiency and real-time performance, can guarantee data integrity and strengthen generalization ability, and can be widely applied in computer technology field.

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 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.

In sum, the disclosed invention constitutes a method and a system for the lossy compression of time series consisting of real numbers, depicted using the IEEE Standard for Floating-Point Arithmetic (IEEE 754). It provides lossy compression of time series using a user-defined maximum tolerable loss per value, aiming to enhance storage and transmission efficiency. This way, it allows the storage of real numbers' time series using the least memory / storage space possible in a computer system, as well as the transmission of real numbers' time series with minimum bandwidth requirements. It allows for case-by-case determination of the acceptable loss so that the outcome reflects the distinct requirements of each case and exploits the similarity of consecutive data points to ensure optimal resources management. The technical result of the invention is the significant economization of storage space and bandwidth as the present invention readily outperforms state-of-the-art methods, leading to more efficient applications.

A system and method for lossy compression and recovery of data are described. The original data is first truncated. The truncated data is then compressed. The compressed truncated data can then be stored and / or transmitted efficiently using fewer bits. To recover the data, the compressed data is then decompressed and the recovered bits are linked. The recovered bits are selected to compensate for the statistical bias introduced by the truncation.