A storage system receives one or more records from a host system. The records are compressed in a first compression format that is native to the host system. The storage system identifies an incompatibility between the first compression format and a first operation of the storage system. In response to the identified incompatibility, the storage system decompresses the received records. The decompression is based on the first compression format. The storage system compresses the decompressed records in a second compression format. The storage system stores the secondarily compressed records onto a storage medium.

Methods, apparatus, systems, and software for implementing self-checking compression. A byte stream is encoded to generate tokens and selected tokens are encoded with hidden parity information in a compressed byte stream that may be stored for later streaming or streamed to a receiver. As the compressed byte stream is received, it is decompressed, with the hidden parity information being decoded and used to detect for errors in the decompressed data, enabling errors to be detected on-the-fly rather than waiting to perform a checksum over an entire received file. In one embodiment the byte stream is encoded using a Lempel-Ziv 77 (LZ77)-based encoding process to generate a sequence of tokens including literals and references, with all or selected references encoded with hidden parity information in a compressed byte stream having a standard format such as DEFLATE or Zstandard. The hidden parity information is encoded such that the compressed byte stream may be decompressed without parity checks using standard DEFLATE or Zstandard decompression schemes. Dictionary coders such as LZ78 and LZW may also be used.

A memory device includes: a plurality of memory cells; soft read logic configured to generate soft data by reading data from the plurality of memory cells in response to a soft read command from a controller, the soft data including at least a major symbol and at least a minor symbol; a compressor configured to generate compressed data by: encoding, into a code alphabet having a second length, a major source alphabet including repetitions of the major symbol by a first length among a plurality of source alphabets included in the soft data, and encoding, into a code alphabet having a longer length than the second length, a minor source alphabet including repetitions of the major symbol by a shorter length than the first length and ending with one minor symbol; and an interface configured to provide the compressed data to the controller.

A method for compressing IQ measurement data obtained from a signal is described. Within the IQ measurement data, at least one block of IQ data is determined where redundancy of the respective data can be exploited. The IQ data of the at least one block is transformed into a transform domain where redundancy of the respective data can be exploited. Transform coefficients obtained in the transform domain are determined and assessed with regard to a pre-defined criteria so as to determine whether the transform coefficients comprise significant information. Only the IQ data assigned to transform coefficients having significant information is stored along with indices in the transform domain indicating where the respective transform coefficients occur in the transform domain.

A storage system receives one or more records from a host system. The records are compressed in a first compression format that is native to the host system. The storage system identifies an incompatibility between the first compression format and a first operation of the storage system. In response to the identified incompatibility, the storage system decompresses the received records. The decompression is based on the first compression format. The storage system compresses the decompressed records in a second compression format. The storage system stores the secondarily compressed records onto a storage medium.

Embodiments of this application provide a data compression method, a data decompression method, and a communication apparatus. The communication apparatus may be a baseband unit (BBU), a remote radio unit (RRU), or the like. The method may be used for data compression and decompression. During data compression, the communication apparatus may obtain first data based on N pieces of original data, an amount K of combined data, and a data bit width L of the combined data, where the first data is data obtained by compressing the original data, a data bit width corresponding to the first data is ?, ? is a non-integer greater than 0, and N, K, and L are positive integers; then determine the combined data based on ? and the first data; and output the combined data.

A control system includes: a generation unit that generates a dataset for each unit section; a feature extraction unit that generates feature quantity data on the basis of the dataset; and a score calculation unit that calculates a score indicating a degree of deviation of the feature quantity data from learning data, by referring to the learning data. The feature quantity data and the score are output as compression results of the dataset. The control system includes a restoration unit that selects pattern data corresponding to a class determined according to the score contained in the compression result, and after adjusting the pattern data using the feature quantity data contained in the compression results, restores the pattern data as a temporal change in the dataset corresponding to the compression results.

A data amount compressing method for compressing a data amount corresponding to a learned model obtained by letting the learning model learn a predetermined data group, the learning model having a tree structure in which multiple nodes associated with respective hierarchically divided state spaces are hierarchically arranged, wherein each node in the learned model is associated with an error amount that is generated in the process of the learning and corresponds to prediction accuracy, and the data amount compressing method includes: a reading step of reading the error amount associated with each node; and a node deleting step of deleting a part of the nodes of the learned model according to the error amount read in the reading step, thereby compressing the data amount corresponding to the learned model.

Providing memory bandwidth compression in chipkill-correct memory architectures is disclosed. In this regard, a compressed memory controller (CMC) introduces a specified error pattern into chipkill-correct error correcting code (ECC) bits to indicate compressed data. To encode data, the CMC applies a compression algorithm to an uncompressed data block to generate a compressed data block. The CMC then generates ECC data for the compressed data block (i.e., an inner ECC segment), appends the inner ECC segment to the compressed data block, and generates ECC data for the compressed data block and the inner ECC segment (i.e., an outer ECC segment). The CMC then intentionally inverts a specified plurality of bytes of the outer ECC segment (e.g., in portions of the outer ECC segment stored in different physical memory chips by a chipkill-correct ECC mechanism). The outer ECC segment is then appended to the compressed data block and the inner ECC segment.

Apparatuses, methods and systems for selective data compression are described. Apparatuses for selective data compression comprise one or modules executable to analyze data to generate analysis results, determine and associate appeal factor values with the data or portions thereof, and compress the data with the compression configurations associated with the appeal factor values. The apparatuses may be employed to compress data on-board manned or unmanned aerial vehicles or other devices. Through use of data analysis, including but not limited to artificial intelligence analysis, image analysis, meta-data analysis, in orbit analysis, and so forth, the manned or unmanned aerial vehicles may compress data collected, generated and/or stored in the vehicle based on data content, data contextual information, data collection opportunity, a priori information, change detection, and/or a particular task the vehicle is tasked to perform (application).