A data compression method, comprising: obtaining a plurality of values of a parameter and an occurrence probability of each of the plurality of values (S101); comparing the occurrence probability with a predetermined threshold, wherein values with the occurrence probability less than the predetermined threshold are first set of values, and values with the occurrence probability greater than or equal to the predetermined threshold are second set of values (S102); performing pretreatment on the first set of values (S103); and encoding the second set of values and the pretreated first set of values (S104). By means of the data compression method, the maximum codeword length can be effectively reduced, so as to reduce the requirements of a code table to the storage space.

Decomposing a value range of the respective syntax elements into a sequence of n partitions with coding the components of z laying within the respective partitions separately with at least one by VLC coding and with at least one by PIPE or entropy coding is used to greatly increase the compression efficiency at a moderate coding overhead since the coding scheme used may be better adapted to the syntax element statistics. Accordingly, syntax elements are decomposed into a respective number n of source symbols s.sub.i with i=1 . . . n, the respective number n of source symbols depending on as to which of a sequence of n partitions into which a value range of the respective syntax elements is sub-divided, a value z of the respective syntax elements falls into, so that a sum of values of the respective number of source symbols s.sub.i yields z, and, if n>1, for all i=1 . . . n1, the value of s.sub.i corresponds to a range of the i.sup.th partition.

[Problem] To suppress increases in the size of a fully indexable dictionary while making it possible for a target bit stream to be subjected to two types of selection operation employing the fully indexable dictionary. [Solution] An information processing device (100) is provided with a storage unit (10) which stores a data structure (11) used to represent a bit stream formed using a first value and a second value. The data structure (11) includes: first data specifying the positions on the bit stream of all or some succession segments including a succession of one or more of the first value or the second value; second data specifying, for some of the succession segments, the number of first values that have appeared on the bit stream from the beginning of the bit stream as far as the succession segment; and third data specifying, for some of the succession segments, the number of second values that have appeared on the bit stream from the beginning of the bit stream as far as the succession segment.

A computer-implemented method according to one embodiment includes dividing data into a plurality of partitions, creating a plurality of groups of different data types within each of the plurality of partitions, independently compressing, within each of the plurality of partitions, each of the plurality of groups of different data types to create a plurality of independently compressed partitions, and validating each of the plurality of independently compressed partitions to create a plurality of validated independently compressed partitions.

A highly programmable data processing unit includes multiple processing units for processing streams of information, such as network packets or storage packets. The data processing unit includes one or more specialized hardware accelerators configured to perform acceleration for various data-processing functions. The data processing unit is configured to retrieve speculative probability values for range coding a plurality of bits with a single read instruction to an on-chip memory that stores a table of probability values. The data processing unit is configured to store state information used for context-coding packets of a data stream so that the state information is available after switching between data streams.

A feed handler is configured to receive a transaction entry from a data feed, the transaction entry indicating at least a floating-point value amount, the data feed associated with a transaction target. The feed handler modifies a locally stored priority queue based on the transaction entry by converting the floating-point value amount in transaction entry to an integer value amount based on a ratio between two underlying components indicated by the transaction target; and storing the integer value amount in a corresponding entry in the locally stored priority queue.

A predictive model utilizes a set of coefficients for processing received input data. To reduce memory usage storing the coefficients, a compression circuit compresses the set of coefficients prior to storage by generating a cumulative count distribution of the coefficient values, and identifying a distribution function approximating the cumulative count distribution. Function parameters for the determined function are stored in a memory and used by a decompression circuit to apply the function the compressed coefficients to determine the decompressed component values. Storing the function parameters may consume less memory in comparison to storing a look-up table for decompression, and may reduce an amount of memory look-ups required during decompression.

A computer-implemented method according to one embodiment includes dividing data into a plurality of partitions, creating a plurality of groups of different data types within each of the plurality of partitions, independently compressing, within each of the plurality of partitions, each of the plurality of groups of different data types to create a plurality of independently compressed partitions, and validating each of the plurality of independently compressed partitions to create a plurality of validated independently compressed partitions.

A method for protecting data in a data memory against an undetected change, wherein a functional variable x is encoded via a value, an input constant, an input signature and a timestamp D into a coded variable, where the functional variable is normalized relative to a base to form the integer value from the functional variable.

Methods and systems for encoding of integers are discussed. For example, various methods and systems may utilize Huffman coding, Tunstall coding, Arithmetic Coding, LZ77 coding, LZ78 coding, LW coding, or Shannon Fano Elias coding to encode the integers.