A data compression method includes: storing data to be written into a first address and a second address into a data buffer in response to a data write request to the first address and the second address of a memory module from a host; according to a relationship between the first address and the second address, selecting a compression scheme from pre-configured compression schemes, and attempting to compress the data to be written into the first address and the second address into compressed data that can be stored into either the first address or the second address by using a pre-defined compression method, if the attempt to compress successes, storing the compressed data into the first address or the second address of the memory module, and identifying the compressed data by using redundant ECC bits to form first identification information.

A compression device for compressing a signal is provided. Said compression device comprises an input for inputting the signal, a processor for performing source separation and/or time frame separation to find recognizable signal components with respect to the signal and/or for determining major characteristics of the signal and/or components of the signal as at least one signal parameter set, a signal generator for generating a known signal on the basis of the at least one signal parameter set, a subtractor for subtracting the known signal from the signal to form a remainder signal, and an output for outputting a compressed signal comprising the remainder signal and the at least one signal parameter set.

This invention is a system and method for encrypted video stream data compaction. The system integrates video processing techniques with data compaction methods, achieving compressed outputs that maintain data security. A video stream processor analyzes frames, estimates motion, and eliminates redundancies, preparing the data for further compression. The processed data is then divided into blocks, mapped to codewords, and subjected to additional compression and encryption rules. This multi-layered approach results in reduced data size and bandwidth requirements for video streaming, while ensuring data integrity and security. The system's adaptability makes it suitable for various video streaming applications, from high-definition content delivery to secure video communications.

Block compression schemes used for image compression are susceptible to generating image blocks having redundant bit sets (i.e. a redundant bit combination), where one of the bit sets in the block is not meaningfully different from the other bit set in the block. As a result, one of the bit sets will be meaningless to a decompression scheme used to decompress the image and thus will not contribute to improving a quality of the decompressed image. The present disclosure provides a technique to exploit redundant bit combinations in a compressed representation of an image, including to exploit more than just the simple case of bit sets that are identical. Exploiting a redundant bit combination will allow an otherwise meaningless bit set to be used for some other discriminating purpose, which can allow for a higher image quality after decompression.

A system and method for processing multimodal data using jointly trained neural compression and enhancement networks. The system efficiently handles temporal, textual, sentiment, and structured data through specialized encoding modules. A fusion module integrates these encodings, capturing cross-modal relationships via attention mechanisms. The fused representation is compressed into a latent space by a trained compression network, then reconstructed and enhanced by a trained reconstruction network and neural enhancement network. Joint training optimizes all components simultaneously using a comprehensive loss function that balances reconstruction quality across modalities with enhancement performance. This approach enables superior data compression, reconstruction, and analysis by leveraging inter-modal correlations across various application domains. The system's latent space exploration capability facilitates generation of synthetic data scenarios for model testing and development.

Certain aspects of the present disclosure provide data compression and channel coding techniques for variable length payloads. A method performed at a wireless node may include obtaining a payload for transmission, compressing the payload using a variable length compression technique to obtain a compressed payload having compressed payload bits of a first length, adding padding bits to the compressed payload to obtain a padded compressed payload. The padded compressed payload comprises information bits including the compressed payload bits and the padding bits. The method further includes encoding the padded compressed payload and outputting the encoded padded compressed payload.

A computing system can include one or more processors and one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the computing system to perform operations including obtaining model structure data indicative of a plurality of parameters of a machine-learned model; determining a codebook comprising a plurality of centroids, the plurality of centroids having a respective index of a plurality of indices indicative of an ordering of the codebook; determining a plurality of codes respective to the plurality of parameters, the plurality of codes respectively comprising a code index of the plurality of indices corresponding to a closest centroid of the plurality of centroids to a respective parameter of the plurality of parameters; and providing encoded data as an encoded representation of the plurality of parameters of the machine-learned model, the encoded data comprising the codebook and the plurality of codes.

The present invention makes it possible to detect an approximation error amount in approximating and encoding axis-dependent data that depends on the coordinate value of each axis of an industrial machine. An approximation error detection device 1 comprises an approximation error amount detection unit 11 that detects an approximation error amount with an absolute value greater than or equal to a predetermined threshold among approximation error amounts in performing model approximation encoding of axis-dependent data on the basis of a part of the axis-dependent data that depends on the coordinate value of each axis of an industrial machine, and on a linear combination model that approximates the axis-dependent data as a linear combination of data on each axis of the industrial machine.

A data compression system compresses original data by a first lossy compression method to generate first compressed data, decompresses the first compressed data to generate first decompressed data, extracts residual information between the original data and the first decompressed data, compresses the residual information by a second lossy compression method different from the first lossy compression method to generate second compressed data, and stores the first compressed data and the second compressed data in the one or more storage devices.

Provided is an encoding technology with which it is possible to encode and compress shaft-dependent data that is dependent on coordinate values of each shaft of an industrial machine. A data encoding device 1 comprising a model approximation encoding unit 11 that, on the basis of some shaft-dependent data that is dependent on coordinate values of each shaft of an industrial machine and a linear combination model that approximates the shaft-dependent data as a linear combination of shaft data pertaining to the industrial machine, generates encoded shaft-dependent data in which the shaft-dependent data is encoded.