Certain implementations of the disclosed technology may include methods and computing systems for performing high-density data compression, particularly on numerical data that demonstrates various patterns, and patterns of patters. According to an example implementation, a method is provided. The method may include extracting a data sample from a data set, compressing the data sample using a first compression filter configuration, and calculating a compression ratio associated with the first compression filter configuration. The method may also include compressing the data sample using a second compression filter configuration and calculating a compression ratio associated with the second compression filter configuration. A particular compression filter configuration to utilize in compressing the entire data set may be selected based on a comparison of the compression ratio associated with the first compression filter configuration and a compression ratio associated with the second compression filter configuration.

The invention describes an encoding scheme for arithmetically encoding a sequence of information values into an arithmetic coded bitstream using providing the bitstream with entry point information allowing for resuming arithmetic decoding the bitstream from a predetermined entry point onward. A respective decoding scheme is also provided. These encoding and decoding schemes provide more efficient encoding concept in view of the decoding speed.

To speed up decoding of a range code. A decompression circuit calculates a plurality of candidate bit values for each bit of the N-bit string based on a plurality of possible bit histories of a bit before a K-th bit in parallel for a plurality of bits, and repeatedly selects a correct bit value of the K-th bit from the plurality of candidate bit values based on a correct bit history of the bit before the K-th bit to decode the N-bit string.

Disclosed are a method and apparatus for storing data. The method includes: acquiring data to be stored; converting the data to be stored from an initial data type to a target data type, a data length corresponding to the target data type being less than that corresponding to the initial data type; and storing the data to be stored of the target data type to a database. In the method according to the present disclosure, a storage space occupied by the data to be stored in the database is greatly reduced. In addition, the method according to the present disclosure is performed prior to lossy or lossless data compression storage of the data to be stored in the related art. That is, on the basis of a compression ratio when the data to be stored is stored in the related art, the present disclosure further improves a compression effect of the data to be stored by reducing the data length when the data to be stored is stored, and further saves storage resources of the database.

A method and apparatus for variable rate compression with a conditional autoencoder is herein provided. According to one embodiment, a method for compression includes receiving a first image and a first scheme as inputs for an autoencoder network; determining a first Lagrange multiplier based on the first scheme; and using the first image and the first Lagrange multiplier as inputs, computing a second image from the autoencoder network. The autoencoder network is trained using a plurality of Lagrange multipliers and a second image as training inputs.

A method and device for compressing FST data are provided. The method includes: acquiring to-be-compressed FST data, where the FST data includes state transition data and state data; decomposing the state transition data based on first data categories to acquire first decomposition data; decomposing the state data based on second data categories to acquire second decomposition data; sequentially arranging, for each of the first data categories, the first decomposition data of the first data category, to acquire first arrangement data of the first data category; alternately arranging the first arrangement data and the second decomposition data according to a sequential order used in the first arrangement data, to acquire second arrangement data; performing classification statistics on the first arrangement data and the second arrangement data to acquire index data; and combining the first arrangement data, the second arrangement data, and the index data, to obtain the compressed FST data.

A method and device for compressing FST data are provided. The method includes: acquiring to-be-compressed FST data, where the FST data includes state transition data and state data; decomposing the state transition data based on first data categories to acquire first decomposition data; decomposing the state data based on second data categories to acquire second decomposition data; sequentially arranging, for each of the first data categories, the first decomposition data of the first data category, to acquire first arrangement data of the first data category; alternately arranging the first arrangement data and the second decomposition data according to a sequential order used in the first arrangement data, to acquire second arrangement data; performing classification statistics on the first arrangement data and the second arrangement data to acquire index data; and combining the first arrangement data, the second arrangement data, and the index data, to obtain the compressed FST data.

Methods and devices for coding point clouds using direct coding mode to code coordinates of a point within a sub-volume associated with a current node instead of a pattern of occupancy for child nodes. Eligibility for use of direct coding is based on occupancy data from another node. If eligible, then a flag is represented in the bitstream to signal whether direct coding is applied to points in the sub-volume or not.

Technologies for compressing a blockchain. In some examples, the technologies include removing selected blocks within a blockchain, and replacing the selected blocks with a summary block and a padding block. Each block of the selected blocks includes data in a certain state (such as data in an obsolete state). The technologies can include generating the summary block and padding blocks according to the data in the selected blocks and an original root hash included in the selected blocks and other blocks of the blockchain. The generating of the summary and padding blocks can include generating a new root hash in the summary and padding blocks that only replaces the original root hash in the summary and padding blocks. The generation of the new root hash can be based on a part of a header of a non-selected block of the blockchain directly linked to an end block of selected blocks.

Technologies for compressing a blockchain. In some examples, the technologies include removing selected blocks within a blockchain, and replacing the selected blocks with a summary block and a padding block. Each block of the selected blocks includes data in a certain state (such as data in an obsolete state). The technologies can include generating the summary block and padding blocks according to the data in the selected blocks and an original root hash included in the selected blocks and other blocks of the blockchain. The generating of the summary and padding blocks can include generating a new root hash in the summary and padding blocks that only replaces the original root hash in the summary and padding blocks. The generation of the new root hash can be based on a part of a header of a non-selected block of the blockchain directly linked to an end block of selected blocks.