A compression coding method, apparatus, and program suitable for continuously coding pieces of fixed length data are provided. The compression coding method includes: dividing, into columns each with a predetermined bit width, records consisting of a fixed-length bit string that includes one or more fields, pieces of data of the same type being contained in the same field among fields determined in advance; and determining, for each column, a probability of occurrence of a bit value in the column at the same position among a plurality of records, and coding the plurality of records on the basis of the probability of occurrence using an entropy coding method.

Adaptive stochastic entropy encoding may include identifying a current portion of an input video stream, and identifying a current probability distribution, which may be an adapted probability distribution associated with a previously encoded portion of the video stream. Adaptive stochastic entropy encoding may include identifying a forward update probability distribution based on the current portion, generating a modified probability distribution for the current portion based on the forward update probability distribution and the current probability distribution, generating an encoded portion based on the current portion and the modified probability distribution, and generating an adapted probability distribution based on the current probability distribution and the forward update probability distribution.

A receiver includes: a filter circuit to generate predicted measurements for a set of tracks based on previous kinematic states of the tracks and timing and source data of next compressed measurements associated to the tracks, generate probability data of differences between the predicted measurements and next measurement data, generate the next measurement data using the predicted measurements and quantized differences between the predicted measurements and the next measurement data, and generate next kinematic states of the tracks based on the previous kinematic states, the timing and source data of the next compressed measurements, and the generated measurement data; a quantizer circuit to quantize the probability data into quantization tables and look up the quantized differences from corresponding indices in the quantization tables; and a decoder circuit to decode encoded index data of the next compressed measurements into the corresponding indices in the quantization tables using the quantized probability data.

An encoder for encoding input data to generate corresponding encoded data is provided. The encoder (10) is operable to process a sequence of elements in the input data from a first element thereof to a last element thereof. The elements have corresponding symbols. The encoder is operable to compute probabilities of symbols present in the sequence, wherein the probabilities of the symbols are computed whilst disregarding those elements of the sequence that have already been encoded into the encoded data. Moreover, the probabilities of the symbols are adaptively changed as the sequence of elements is progressively encoded into the encoded data. Furthermore, information describing the probabilities is delivered; optionally, the probabilities of the symbols are accompanied with additional information indicating how the probabilities are adaptively changing. There is also provided a decoder for performing an inverse of encoding performed by the encoder.

Methods for estimating cost savings in a storage system using an external host system. One method includes accessing over a communication network data from a unit of storage of a data storage system, wherein each of the blocks of data is uncompressed. A plurality of blocks is parsed from the data. A plurality of fingerprints is generated from the blocks using a hash algorithm. A deduplication ratio is estimated for the plurality of blocks stored in the unit of storage using a hyperloglog algorithm and a first plurality of buckets compartmentalizing the plurality of blocks, wherein the first plurality of buckets is defined by precision bits of the plurality of fingerprints. An effective compression ratio is estimated for the plurality of blocks stored in the unit of storage using the hyperloglog algorithm and a second plurality of buckets compartmentalizing the plurality of blocks, wherein the second plurality of buckets is defined by ranges of compression ratios.

An encoder includes processing circuitry, a block memory, and a frame memory. The processing circuitry defines at least one parameter for each of plural types of segment_ids, splits an image into blocks, assigns, to each of the blocks, segment_id according to a type of the block, among the plural types of segment_ids, and sequentially encodes the blocks. In encoding the blocks, the processing circuitry identifies segment_id of a current block to be encoded, and encodes the current block using the at least one parameter defined for identified segment_id. The at least one parameter includes seg_context_idx for identifying probability information associated with context used in context-based adaptive binary arithmetic coding (CABAC).

A system and method for data compression with protocol adaptation, that utilizes a codebook generator which leverages one or more machine/deep learning algorithms trained on at least a plurality of protocol policies in order to generate a protocol appendix and codebook, wherein original data is encoded by an encoder according to the codebook and sent to a decoder, but instead of just decoding the data according to the codebook to reconstruct the original data, data manipulation rules such as mapping and transformation are applied at the decoding stage to transform the decoded data into protocol formatted data.

A system and method for data compression with homomorphic encryption, which enables secure storage of private information in a database, and which enables searching and comparison of encrypted data within the database, comprising a stream condition system configured to optimize the contents of received data for lossless compression by a data encoder, a data encoder to perform the lossless compression, and an encrypted search engine configured to encrypt the compressed data according to a homomorphic encryption scheme and store the encrypted data in a database. The system may receive a data query and encrypt the data query according to the homomorphic encryption scheme. The encrypted data query may be compared against an encrypted element in the database and an encryption score generated. The encryption score may be compared against a set of criteria to determine if a match is found. Matched data may be returned to the requesting entity.

This application relates to the field of artificial intelligence, and discloses a data encoding method, a data decoding method, and data processing apparatuses. Both the data encoding method and the data decoding method relate to an invertible flow-based model. The invertible flow-based model includes a target invertible flow layer, a model parameter of the target invertible flow layer is used to constrain an auxiliary variable generated in an inverse transform processing process, an operation corresponding to the target invertible flow layer includes a multiplication operation and a division operation that are determined based on the model parameter, and the auxiliary variable is an increment of a product of the multiplication operation or a remainder generated through the division operation.

A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values used to correct predicted attribute values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points and distances between a particular point for whom an attribute value is being predicted and the neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. A decoder follows a similar prediction process as an encoder and corrects predicted values using attribute correction values included in a compressed attribute information file.