A computer-implemented method includes: obtaining input data comprising a plurality of data values, wherein each data value is associated with a time value; obtaining a plurality of threshold criteria; and (a) selecting, or generating, a dataset or a plurality of datasets that are different to one another from the input data, and determining whether each selected, or generated dataset meets a threshold criterion of the plurality of threshold criteria; and (b) causing each selected, or generated, dataset that meets the threshold criterion to be stored in memory in association with the threshold criterion, repeating (a) and (b) for each threshold criterion of the plurality of threshold criteria, thus causing a plurality of datasets to be stored in the memory, wherein each stored dataset meets a threshold criterion of the plurality of threshold criteria.

Multiple parallel prediction functions are employed for content-aware data compression. An exemplary method comprises obtaining a floating point number comprising a sign, an exponent at a given base and a significand having a length; applying a plurality of distinct prediction algorithms to the floating point number to generate a corresponding plurality of predictions; selecting a given one of the plurality of distinct prediction algorithms for the floating point number by evaluating a compression metric applied to the plurality of predictions; and encoding the floating point number by encoding the exponent and the length as a single code using a residual generated by the selected prediction algorithm. A disambiguation index optionally identifies the selected prediction algorithm among a set of prediction algorithms that potentially generated the selected prediction. A set of one or more predictors out of a larger set of predictors can be determined for a specific data set based on a performance-based ranking.

An information processing device includes: a memory configured to store data concerning a write access; and a processor coupled to the memory, the processor being configured to: record, for each data, time of a write access of the data to management information, when writing out data from the memory to a storage, determine a plurality of data as a group of overall compression based on the management information, the plurality of data having a difference of time of write accesses being equal to or less than a threshold value, and compress the plurality of data corresponding to the determined group by an overall compression, and write compressed data obtained through the overall compression to the storage.

The present invention is mainly to solve the technical problems of the prior art existed. The present invention relates to compression, in particular to an improved real-time adaptive data compression for efficient data storage. An aspect of present disclosure relates to a method for managing data storage in a data storage system. The method includes the steps of determining, by a processor of said data storage system, receipt of one or more blocks of data for storage; identifying, by the processor, a compression technique for storage of said one or more blocks of data; and compressing in-line or post processing, by the processor, if said compression technique is an in-line compression technique for writing the data in a memory, said one or more blocks of data based at least on a resources utilization of said data storage system.

The systems and methods described herein can pre-process a blendshape matrix via a global clusterization process and a local clusterization process. The pre-processing can cause the blendshape matrix to be divided into multiple blocks. The techniques can further apply a matrix compression technique to each block of the blendshape matrix to generate a compression result. The matrix compression technique can comprise a matrix approximation step, an accuracy verification step, and a recursive compression step. The compression result for each block may be combined to generate a compressed blendshape matrix for rendering a virtual entity.

Embodiments of the present invention include a compression system including one or more compressibility inputs; a compression predictor configured to predict the compressibility of data based on the one or more compressibility inputs; a compressor configured to compress the data; and one or more compression inputs. At least one of the compression predictor or the compressor is configured to determine how to compress the data based on the one or more compression inputs. The one or more compressibility inputs comprise at least one of an object extension dictionary, an object signature dictionary, and an entropy estimation algorithm library. The one or more compression inputs comprise at least one of a quality of service profile, a device performance profile and a compression algorithm performance chart.

One aspect of the disclosure provides a device, comprising: an allocation module, for determining one or more metrics of each of a plurality of data streams; a compression module, for compressing each of the plurality of data streams and generating a plurality of compressed data streams, the compression module applying a compression ratio that varies as a function of the metrics determined by the allocation module; and a buffer memory, for storing the plurality of compressed data streams.

A matrix compression/decompression accelerator (MCA) system/method that coordinates lossless data compression (LDC) and lossless data decompression (LDD) transfers between an external data memory (EDM) and a local data memory (LDM) is disclosed. The system implements LDC using a 2D-to-1D transformation of 2D uncompressed data blocks (2DU) within LDM to generate 1D uncompressed data blocks (1DU). The 1DU is then compressed to generate a 1D compressed superblock (CSB) in LDM. This LDM CSB may then be written to EDM with a reduced number of EDM bus cycles. The system implements LDD using decompression of CSB data retrieved from EDM to generate a 1D decompressed data block (1DD) in LDM. A 1D-to-2D transformation is then applied to the LDM 1DD to generate a 2D decompressed data block (2DD) in LDM. This 2DD may then be operated on by a matrix compute engine (MCE) using a variety of function operators.

A FRAMEWORK and the associated method, schema and design for processing digital data, whether random or not, through encoding and decoding losslessly and correctly for purposes including the purposes of encryption/decryption or compression/decompression or both. There is no assumption of the digital information to be processed before processing. A Universal Coder is invented and now Pigeonhole meets Blackhole.

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive metadata and results including at least one data transmission constraint, perform a search of a k-d tree based on the at least one data transmission constraint, and select at least one data compression process from the k-d tree based on the search. The k-d tree includes a plurality of nodes corresponding to data compression solutions.