A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.

An information processing device and method for enabling partial control of the resolution of a data group that can be turned into a tree structure. Data of an Octree pattern is encoded, so that a bit stream containing depth control information indicating that a leaf node is to be formed at a different level from the lowest level based on information specifying the depth of the Octree pattern is generated. Also, a bit stream is decoded, so that an Octree pattern including a leaf node at a different level from the lowest level is constructed, on the basis of depth control information indicating that the leaf node is to be formed at a different level from the lowest level based on information specifying the depth of the Octree pattern. The present disclosure can be applied to an information processing device, an image processing device, an electronic apparatus, an information processing method, a program, or the like, for example.

In some embodiments, a method analyzing a first set of values for a first bin plane in a plurality of bin planes. The plurality of bin planes are used to determine a context model for entropy coding of a current block in a video. The method determines whether to use a second set of values from a second bin plane based on the analyzing. When it is determined to use the second set of values, information is calculated for the context model using the first set of values and the second set of values. When it is determined to not use the second set of values, information is calculated for the context model using the first set of values.

A method is performed by a network element (NE) in a network implementing an Interior Gateway Protocol (IGP). The method comprises generating a message comprising a header and data, wherein the header comprises a length of the data prior to compressing the data, a length of the data after compressing the data, and a compression identifier, compressing the data based on a compression scheme identified by the compression identifier to obtain compressed data, and forwarding a compressed message comprising the header and the compressed data to another NE in the network

Embodiments of the present application provide an occupancy information prediction method, an encoder, a decoder, and a storage medium. The occupancy information prediction method comprises: when an encoder encodes geometrical information on the basis of an octree, determining encoding information corresponding to a neighboring node of a node to be predicted, and a distance parameter between a child node of the node to be predicted and the neighboring nodes; wherein the encoding information corresponding to the neighboring node comprises occupancy information; determining an occupancy weight corresponding to the child node of the node to be predicted according to the distance parameter, and the encoding information corresponding to the neighboring node; performing a prediction processing on the child node according to the occupancy weight and a preset occupancy threshold set to obtain a node type corresponding to the child node.

An embodiment of a video codec may include technology to store video data, and utilize a generalized bypass bin to one or more of encode and decode the video data. Other embodiments are disclosed and claimed.

There is provided a system and method for compression and decompression of a data stream used by machine learning networks. The method including: encoding each value in the data stream, including: determining a mapping to one of a plurality of non-overlapping ranges, each value encoded as a symbol representative of the range and a corresponding offset; and arithmetically coding the symbol using a probability count; storing a compressed data stream including the arithmetically coded symbols and the corresponding offsets; and decoding the compressed data stream with arithmetic decoding using the probability count, the arithmetic decoded symbols use the offset bits to arrive at a decoded data stream; and communicating the decoded data stream for use by the machine learning networks.

A method and a system for decoding MPEG-G encoded data of genomic information, including: receiving MPEG-G encoded data; extracting encoding parameters; selecting an arithmetic decoding type based upon the extracted encoding parameters; selecting a predictor type specifying the method to obtain probabilities of symbols which were used for arithmetically encoding the data, based upon the extracted encoding parameters; selecting arithmetic coding contexts based upon the extracted encoding parameters; and decoding the encoded data using the selected predictor and the selected arithmetic coding contexts.

A system collects statistical data for a data page, divides the data page into parts, analyzes the data page and the statistical data, based on compression efficiency of one or more compression methods for each part of each page, to determine a compression method for each part of page, and compresses, based on the analyzing, the parts of the data page.

A method, computer program product, and system includes a processor obtaining data including values and generating a value conversion dictionary by applying a parse tree based compression algorithm to the data, where the value conversion dictionary includes dictionary entries that represent the values. The processor obtains a distribution of the values and estimates a likelihood for each based on the distribution. The processor generates a code word to represent each value, a size of each code word is inversely proportional to the likelihood of the word. The processor assigns a rank to each code word, the rank for each represents the likelihood of the value represented by the code word; and based on the rank associated with each code word, the processor reorders each dictionary entry in the value conversion dictionary to associate each dictionary entry with an equivalent rank, the reordered value conversion dictionary comprises an architected dictionary.