The present disclosure relates to system(s) and method(s) for real time compression of a data frame. The system receives the data frame comprising a set of symbols. Further, the system identifies frequency of each symbol, from the set of symbols. The system further sorts the symbols in descending order of frequency, associated with each symbols. Further, the system computes a compression gain associated with each predefined case type, a set of predefined case types. Furthermore, the system selects a target predefined case type, based on the comparison of the compression gain of each predefined case types. The system further assigns a compressed code to Most Frequent Symbols (MFS), in the data frame. The compressed code is assigned based on the target predefined case type. Further, the system generates a compressed frame, associated with the data frame. The compressed frame comprises a header and a sequence of compressed symbols.

A method for sharing a hardware decompression engine, including performing a compression type check on a first data stream to determine a compression type of the first data stream, wherein the first data stream is compressed using one selected from a group consisting of a first compression type and a second compression type; wherein, when the first data stream is compressed with the second compression type: receiving the second compression type at a selector; converting the first data stream compressed with the second compression type into a second data stream of the first compression type; inputting the converted second data stream into the selector; and decompressing the converted second data stream using the hardware decompression engine capable of decompressing a data stream compressed using the first compression type. In other aspects, a system for sharing a hardware decompression engine and a computing system are provided.

Methods and systems for improving coding decoding efficiency of video by providing a syntax modeler, a buffer, and a decoder. The syntax modeler may associate a first sequence of symbols with syntax elements. The buffer may store tables, each represented by a symbol in the first sequence, and each used to associate a respective symbol in a second sequence of symbols with encoded data. The decoder decodes the data into a bitstream using the second sequence retrieved from a table.

Lossless compression of a content item using a neural network trained on content item cohorts. A computing system includes a neural network that is used to train a plurality of symbol prediction models. Each symbol prediction model is trained based on a corresponding cohort of content items. A particular symbol prediction model of the models trained is selected based on an intrinsic characteristic of a particular content item to be losslessly compressed such as, for example, the type or file extension of the content item. The content item is then losslessly compressed based on a set of symbol predictions fed to an arithmetic coder that are generated using the particular symbol prediction model selected.

The present disclosure relates to system(s) and method(s) for real time compression of a data frame. The system receives the data frame comprising a set of symbols. Further, the system identifies frequency of each symbol, from the set of symbols. The system further sorts the symbols in descending order of frequency, associated with each symbols. Further, the system computes a compression gain associated with each predefined case type, a set of predefined case types. Furthermore, the system selects a target predefined case type, based on the comparison of the compression gain of each predefined case types. The system further assigns a compressed code to Most Frequent Symbols (MFS), in the data frame. The compressed code is assigned based on the target predefined case type. Further, the system generates a compressed frame, associated with the data frame. The compressed frame comprises a header and a sequence of compressed symbols.

Systems and methods for encoding and compressing structured data for rendering computer-generated graphics in three-dimensional applications are described herein. In various implementations, structured data may be encoded into an intermediate symbol stream using frequency tables specifically for a type of structured data object, field, or value being encoded. In some implementations, additional information may be incorporated into the symbols used to encode individual structured data objects. In some implementations, structured data objects may be encoded based on a context associated with the structured data object. Once structured data is encoded as an intermediate symbol stream, the intermediate symbol stream may be compressed using one or more entropy coding methods. By taking into account the underlying structure of the data, the systems and methods described herein reduce redundancies, thereby improving compression of the structured data.

Data to be stored in a data block for a columnar database table may be compressed according to a multi-level compression scheme. Data to be stored in the data block may be received. The data may be compressed according a column-specific compression technique to produce compressed data. The compressed data may then be compressed according to a second compression technique different than the column-specific compression technique to produce multi-level compressed data. The multi-level compressed data may be stored in the data block. When reading from the data block, multi-level compressed data may be decompressed according to the column-specific compression technique and the default compression technique applied to the data.

Methods and systems for improving coding decoding efficiency of video by providing a syntax modeler, a buffer, and a decoder. The syntax modeler may associate a first sequence of symbols with syntax elements. The buffer may store tables, each represented by a symbol in the first sequence, and each used to associate a respective symbol in a second sequence of symbols with encoded data. The decoder decodes the data into a bitstream using the second sequence retrieved from a table.

The proposed approach deals with efficient transmission for distributed AI with a provision to switch among multiple bandwidths. During the distributed inference at edge devices, each device needs to load part of the AI model only once, but the input/output features communicated between them can be flexibly configured depending on the available transmission bandwidth by enabling/disabling connection between nodes in the Dynamic feature size Switch (DySw). When some nodes are connected or disconnected in order to achieve the desired compression factor, other parameters of the DNN remain the same. That is, the same DNN model is used for different compression factors, and no new DNN model needs to be downloaded to adapt to the compression factor or the network bandwidth.

A method of data compression includes obtaining binary sensor data having rows with multi-bit data samples. The rows are divided into data groups each including two or more samples. A precedent value is selected for the rows or respective precedent values are selected for each data group. A compressed row of compressed sensor data is generated from each row by calculating differences between the data sample and the precedent value for its associated data groups. A Compression Information Packet (CIP) is generated for each row including information for returning the binary sensor data that includes a compressed predicate indicating whether each data group is stored compressed, a data group size being a multi-bit value that stores a group size used for row compression, and a compressed word size that stores a dynamic range of the row compression. The compressed rows are stored as stored compressed data along with the CIPs.