There is provided a computer implemented method of compressing a baseline dataset comprising a sequence of a plurality of instances of a plurality of unique data elements, the method comprising: providing a weight function that calculates an increasing value for a weight for each one of the plurality of instances of each one of the plurality of unique data elements in the baseline dataset, as a function of increasing number of previously processed sequential locations of each of the plurality of instances of each respective unique data element within the baseline dataset relative to a current sequential location of the baseline dataset, computing an encoding for the baseline dataset according to a distribution of the weight function computed for the plurality of unique data elements in the baseline dataset, and creating a compressed dataset according to the encoding.

A method of encoding input data includes dividing the input data into a plurality of data packets, an input packet of the plurality of data packets including a plurality of digits in a first base system, base-converting the input packet from the first base system to generate a base-converted packet including a plurality of converted digits in a second base system, the second base system having a base value lower than that of the first base system, and incrementing the converted digits to generate a coded packet for transmission through a communication channel.

A data output method, a data acquisition method, a device, and an electronic apparatus are provided, and a specific technical solution is: reading a first data sub-block, and splicing the first data sub-block into a continuous data stream, wherein the first data sub-block is a data sub-block in transferred data in a neural network; compressing the continuous data stream to acquire a second data sub-block; determining, according to a length of the first data sub-block and a length of the second data sub-block, whether there is a gain in compression of the continuous data stream; outputting the second data sub-block if there is the gain in the compression of the continuous data stream.

Example methods, apparatus, and articles of manufacture to compress telematics data are disclosed herein. An example computer-implemented method includes identifying, using one or more processors, a portion of recorded telematics data representing a physical transversal of a physical intersection of two or more road segments, wherein each road segment has an assigned unique ordinal value; identifying, using one or more processors, a first road segment on which the physical transversal entered the intersection; identifying, using one or more processors, a second road segment on which the physical transversal exited the intersection; identifying, using one or more processors, a pair of ordinal values including a first ordinal value assigned to the first road segment, and a second ordinal value assigned to the second road segment; and storing the pair of ordinal values instead of the portion of the recorded telematics data in a compressed representation of the recorded telematics data.

Systems and methods provide for efficiently and accurately determining a simplified path that conforms to the geometry of an original path by simultaneously minimizing the deviation from the original path and reducing the number of anchor points in the simplified path. A simplified path may be iteratively generated by updating parametric values and anchor points for candidate simplified paths at epochs. A deviation in distance between points on the original path and corresponding points on candidate paths may be iteratively decreased to ensure that the resulting simplified path follows the geometry of the original path to a predetermined threshold. Continuity constrains can also be applied to ensure smoothness of the simplified path.

Aspects of the disclosure provide methods and apparatuses for neural network model compression/decompression. In some examples, an apparatus for neural network model decompression includes receiving circuitry and processing circuitry. The processing circuitry decodes, from a bitstream corresponding to a representation of a neural network, at least a syntax element to be applied to multiple blocks in the neural network. Then, the processing circuitry reconstructs, from the bitstream, weight coefficients in the blocks based on the syntax element.

Systems and methods providing a hybrid intermediate stream format are provided. The method includes compressing a vertex into a first data block via a first compression method, compressing the vertex into a second data block via a second compression method, determining a smaller file of the first data block and the second data block, finalizing compression of the vertex via a compression method, selected from the first compression method and the second compression method, corresponding to the determined smaller file of the first data block and the second data block, and transferring the compressed vertex.

Example methods, apparatus, and articles of manufacture to capture and compress telematics data are disclosed herein. An example computer-implemented method, executed by a processor, to represent telematics data includes identifying, with the processor, a physical intersection of roads, identifying, with the processor, virtual lines crossing the roads, assigning, with the processor, ordinals to the virtual lines, representing, with the processor, a physical traversal through the physical intersection captured in first telematics data by a pair of the ordinals, and storing the pair of the ordinals in second compressed telematics data.

An information handling system includes a processor configured to process a training data file to determine an optimal data compression algorithm. The processor may also perform a compression ratio analysis that includes compressing the training data file using data compression algorithms, calculating a compression ratio associated with each of the data compression algorithms, determining an optimal compression ratio from the compression ratio associated with the each data compression algorithm; and determining a desirable data compression algorithm associated with the training data file based on the optimal compression ratio. The processor may also perform a probability analysis that includes generating a symbol transition matrix based on the desirable data compression algorithm, extracting statistical feature data based on the symbol transition matrix, and generating probability matrices based on the statistical feature data to determine the optimal data compression algorithm for each segment of a working data file.

A decoder includes an entropy decoder configured to derive a number of bins of the binarizations from the data stream using binary entropy decoding by selecting a context among different contexts and updating probability states associated with the different contexts, dependent on previously decoded portions of the data stream; a desymbolizer configured to debinarize the binarizations of the syntax elements to obtain integer values of the syntax elements; a reconstructor configured to reconstruct the video based on the integer values of the syntax elements using a quantization parameter, wherein the entropy decoder is configured to distinguish between 126 probability states and to initialize the probability states associated with the different contexts according to a linear equation of the quantization parameter, wherein the entropy decoder is configured to, for each of the different contexts, derive a slope and an offset of the linear equation from first and second four bit parts of a respective 8 bit initialization value.