71 results about "Lossy compression" patented technology

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 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.

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 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.

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 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.

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.

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.

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 present application discloses a hyperspectral image lossy compression method based on hierarchical prediction from the fact that hyperspectral image has both spatial correlation and spectral correlation, and JPEG2000 only eliminates the spatial redundancy of hyperspectral image. The present application adopts two-dimensional wavelet transform to remove spatial redundancy, and utilizes the spectral correlation of hyperspectral image to generate high-quality SI at the decoding end to reduce the code rate required by channel coding. The hyperspectral image is divided into GOP area and non_GOP area, and the WZ waveband in the GOP area adopts hierarchical prediction structure to generate SI; the WZ waveband in the non_GOP area will be specially processed, and only one decoded waveband in the front direction is used to generate SI. The hyperspectral image lossy compression method based on hierarchical prediction disclosed by the present application can obtain better rate-distortion performance in the full code rate segment compared with JPEG2000.

Method for providing validation data (23) in a data storage system (12), wherein the validation data (23) are intended for the validation of driver assistance systems (4); wherein the data storage system (12) is configured to provide the validation data (23) for the validation of at least one driver assistance system (4); and wherein the validation data (23) comprise at least some data (3) recorded by sensors (2) in vehicles during test drives; comprising the following steps: a) receiving the validation data; b) performing compression (6) of the validation data to produce compressed validation data (11) using a lossy compression method; c) generating synthesized validation data (20) based on the compressed validation data (11) produced in step b);andd) Performing a comparison of the validation data with the synthesized validation data (20), wherein a check is carried out to see whether a deviation between the synthesized validation data (20) and the validation data received in step a) is within a specified tolerance range, wherein the tolerance range is chosen such that deviations are detected which are within a signal noise caused by properties of at least one sensor (2) with which validation data were recorded, wherein the tolerance range is further chosen such that the distance between synthesized validation data (20) and the recorded reality is not greater than the distance between the validation data received in step a) and the same reality;ande) Storing the compressed validation data (11) in the data storage system (12) if, in step d), it was determined that the deviation is within the specified tolerance range.;

Post training compression of 3DGS data is agnostic to training in a traditional signal compression perspective. Gaussian parameters are treated as signals. Pre-processing and transform coding techniques are used to compress the signals effectively. Firstly, lossless / lossy compression is performed on 3DGS geometry (positions, scales, rotations) using a point cloud coding-based (e.g., G-PCC, GeS) framework. Positions are compressed using occupancy tree coding. Scales and rotations are encoded as attributes using transform coding. The widely used block-based graph Fourier transform (GFT) is used to compress the attributes (base colors, spherical harmonic coefficients and opacities). In addition, a graph construction strategy is used for 3DGS data that computes the edge weights based on similarity (or dissimilarity) between the 3D Gaussian distributions using KL-divergence. Alternatively, positions can be encoded using occupancy tree (e.g., G-PCC, GeS) or AI-based PCC methods, and any subset of Gaussian parameters or the transformed coefficients of Gaussian parameters can be mapped into 2D frames and encoded by video coders.

The present application relates to video image communication, in particular to a kind of highway traffic safety monitoring video processing device and system, comprising: by video acquisition module, obtain road traffic video image data, utilize video compression preprocessing module, by analyzing the importance degree of each part area in video image and the important degree of loss compression threshold value obtained in video image data LBP value and transmittance are compressed before the preprocessing of video image data, and by video encoding compression module, video image communication module, video decompression module, video storage module, alarm module and visualization module realize the efficient compression and transmission data of road traffic video image and accurate analysis and early warning.The present application can protect important data while preprocessing video image to improve the compression efficiency of video image, ensure the reliability of subsequent video data analysis result, obtain accurate and reliable warning signal.

Method for storing validation data (23) in a data storage system (12), wherein the validation data (23) are intended for validating systems for processing large amounts of data; wherein the data storage system (12) is configured to provide the validation data (23) for validating at least one system for processing large amounts of data; and wherein the validation data (23) comprise at least some data recorded by sensors (2); comprising the following steps: a) receiving the validation data; b) determining (7) at least one statistical property of the received validation data; c) performing compression (6) of the validation data to produce compressed validation data (11) using a lossy compression method; d) determining at least one statistical comparison property;e) Performing a comparison of the at least one statistical property with the at least one statistical comparison property, wherein the comparison checks whether a deviation between synthesized validation data (20) producible on the basis of the compressed validation data and the validation data received in step a) lies within a predetermined tolerance range, wherein the tolerance range is chosen such that deviations are detected which lie within a signal noise caused by properties of at least one sensor (2) with which validation data were recorded, wherein the tolerance range is further chosen such that the distance between validation data (20) synthesized on the basis of the compressed validation data (11) and the recorded reality is not greater than the distance between the validation data received in step a) from the same reality;and f) Storing the compressed validation data (11) in the data storage system (12) if, in step e), it was determined that the deviation is within the specified tolerance range.;

The invention discloses a hyperspectral image compression network and compression method based on multi-scale spectrum and spatial feature enhancement, and mainly solves the problems of insufficient hyperspectral image spectrum modeling, insufficient spatial feature extraction and high network complexity in the prior art. The network comprises a main encoder, a main decoder, a super-prior encoder, a super-prior decoder and an entropy model. The main encoder comprises a spectral attention gating data unit, a convolution unit and a multi-scale spatial adaptive feature attention enhancement unit, and is used for converting an input image into potential representation and removing spatial and spectral redundancy; the main decoder and the main encoder are symmetrical in structure; the super-prior encoder comprises a convolution layer and an activation layer and is used for extracting auxiliary information from the output of the main encoder; the super-prior decoder and the super-prior encoder are symmetrical in structure; the entropy model is Gaussian distribution based on output parameters of a super-prior decoder. After the network is trained, lossy compression of a hyperspectral image can be realized. The method reduces the network complexity and spectral distortion, improves the reconstruction quality of complex ground feature details, and is suitable for earth observation, meteorological monitoring and the like.

A service compresses a data set in accordance with a data compression rate, resulting in generation of compressed data having a first data quality. The service decompresses the compressed data, resulting in generation of decompressed data. The service filters, from the decompressed data, data that is identified as belonging to a selected class of data. The service causes an ML classifier to perform a classification operation on the data that is identified as belonging to the selected class of data. The service determines a classification accuracy of the classification operation performed by the ML classifier. The service determines whether the classification accuracy at least meets a threshold accuracy requirement. If the threshold is met, the data compression rate is increased; otherwise, it is decreased.

A method and apparatus for a dynamic determination of a data compression and decompression method in a neural network model are provided. The apparatus for a dynamic determination of a data compression method computes an importance value based on input data and information related to the input data, determines, based on the importance value, whether to perform lossy compression or lossless compression on the input data, and performs, using the compression parameter, the lossy compression or the lossless compression on the input data, based on a result of the determination. In addition, the apparatus for a dynamic determination of a data decompression method decompresses data that is compressed by the apparatus for a dynamic determination of a data compression method.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating compressed representations of synthetic images. One of the methods is a method of generating a synthetic image using a generative neural network, and includes: generating, using the generative neural network, a plurality of coefficients that represent the synthetic image after the synthetic image has been encoded using a lossy compression algorithm; and decoding the synthetic image by applying the lossy compression algorithm to the plurality of coefficients.

The application discloses a kind of intra block copy cache unit and method, cache unit includes reconstruction cache area and compressed cache area;Reconstruction cache area is used to store the reconstruction pixel block obtained by target coding unit for encoding processing, and / or the compressed block obtained by dividing reconstruction pixel block;Compressed cache area is used to receive the compressed bitstream obtained by compressed block for compression processing, and sequentially stores compressed bitstream according to the time identifier of compressed bitstream;Compression processing is the loss compression of compression rate 50%.The application enables the cache of intra block copy to apply the compression technology based on block, and through the cooperation of reconstruction cache area and compressed cache area, with the encoding process, the reference pixel information of cache is updated, so that more reference blocks are cached under the size limit of the cache of intra block copy, and fast access to reference block is realized, which can be widely applied in the field of video coding technology.

The invention discloses a time sequence data adaptive processing method and system, electronic equipment and a medium, and relates to the technical field of industrial control systems and database storage. The method specifically comprises the following steps: entering a discharge mode in response to an experiment start trigger signal, pausing persistent writing and directly writing time sequence data into a memory buffer area in a binary stream manner; entering a sorting mode in response to the ending trigger signal, reading data and calculating a change rate index; comparing the rate of change indicator with a decision threshold to identify a transient interval and a steady interval; and performing lossless processing on the transient interval data, performing lossy compression on the steady-state interval data, and finally writing the data into persistent storage equipment. The invention aims to eliminate write jitter through a dynamic and static separation mechanism, and realize lossless hierarchical storage of key data and high compression ratio of stable data based on physical characteristics.

The invention belongs to the technical field of video monitoring and data security, and particularly relates to an anonymized video recording and restoring method and system based on container reversible restoration. The recording method comprises the following steps: performing anonymization processing on a sensitive area; encoded reference pixel data (such as a local reconstructed frame) is obtained, spatial domain differential pixel values are calculated based on the data and development levels are divided, content binding hash is constructed, and it is encrypted and packaged in an independent metadata track of a container. The restoration method comprises the following steps: executing file integrity and content binding dual hash verification on the record file, executing spatial domain differential recovery only when verification is consistent, and forcibly keeping an anonymization state if verification is consistent. According to the method, lossy compression noise is eliminated by using a reconstruction frame calibration mechanism, and lossless restoration independent of a coding format is realized; through dual verification and a rolling key mechanism, splicing attacks and frame extraction attacks are effectively prevented, and the integrity and security of an evidence chain are ensured.

Methods, systems and apparatuses provide for encoder technology that conducts a spatial transformation on tiles in a block of an image, wherein the spatial transformation is conducted on a per tile basis and results in a first sub-band data and second sub-band data, predicts residual data from the first sub-band data, and generates quantization data from the second sub-band data, wherein the residual data and the quantization data represent a lossy compressed portion of the image. Additionally, decoder technology may recover first sub-band data from residual data, scale up to second sub-band data from quantization data, wherein the residual data and the quantization data represent a lossy compressed portion of an image, and conduct an inverse spatial transformation on the first sub-band data and the second sub-band data, wherein the inverse spatial transformation is conducted on a per tile basis and results in tiles in a block of the image.

The invention provides an end-to-end voice encryption method and device suitable for a multi-hop lossy channel and a Bluetooth headset, and the method comprises the steps: carrying out the front-end noise reduction, framing and segmentation of collected voice at a transmitting end, automatically selecting a group public key or a point-to-point private key from a pre-configured key set as a main key according to a session mode, and carrying out the end-to-end voice encryption of the multi-hop lossy channel. And performing time sequence scrambling, segment-by-segment fast Fourier transform and sub-key-based frequency domain encryption on a plurality of time domain sub-segments in each frame according to the master key, generating encrypted voice through inverse transformation and overlapped weighted smooth splicing, and transmitting the encrypted voice through a multi-hop voice communication channel containing multi-stage lossy coding and decoding. Framing segmentation, transformation, inverse frequency domain encryption and inverse time domain scrambling which are opposite to the above process are executed at a receiving end, and noise reduction is combined with the U-shaped neural networks at the front end and the rear end to recover the voice to be understandable. According to the invention, flexible key management and high-quality secure voice transmission can be realized in a scene in which a group call and a point-to-point private call coexist and are subjected to lossy compression for multiple times.

A distance calculation system and method for clustering algorithms are proposed. Based on the information distance theory with Coriolis complexity, a unified method for information distance calculation, lossless compressed distance calculation, and lossy compressed distance calculation is designed, and a compressed distance calculation system under lossy conditions is formed. This solves the problem of lossy compressed distance calculation for continuous data such as images and videos, and forms a complete and unified compressed distance calculation method and system applicable to any data type.

The present application relates to the technical field of image processing, and particularly relates to a heterogeneous fidelity compression method based on image data clustering. The present application provides a heterogeneous fidelity compression method based on image data clustering, comprising: performing high-low bit depth splitting on an input infrared image to obtain high-bit data and low-bit data; performing JPEG-LS lossless compression on the high-bit data and H.264 intra-frame lossy compression on the low-bit data to obtain a first compressed code stream and a second compressed code stream respectively; and uniformly packaging the first compressed code stream and the second compressed code stream to form a unified compressed stream, wherein the unified compressed stream is transmitted or stored. The present scheme can simultaneously meet the requirements of high compression ratio, key gray scale fidelity and embedded real-time implementation in infrared image compression.

A method of compressing data is described in which the compressed data is generated by either or both of a primary compression unit or a reserve compression unit in order that a target compression threshold is satisfied. If a compressed data block generated by the primary compression unit satisfies the compression threshold, that block is output. However, if the compressed data block generated by the primary compression unit is too large, such that the compression threshold is not satisfied, a compressed data block generated by the reserve compression unit using a lossy compression technique, is output.

A method includes obtaining time series data that includes a series of data points listed in temporal order. The method includes determining that a size of the time series data fails to satisfy a threshold size. In response, the method includes determining a range of the series of data points and determining, using the range of the series of data points, a respective score for each respective data point in the series of data points. The method also includes removing, using the respective scores for each data point in the series of data points, a plurality of data points from the series of data points. After removing the plurality of data points from the series of data points, the method includes determining an updated size of the series of data points and determining that the updated size of the series of data points satisfies the threshold size.