A data compression apparatus is described which has an encoder configured to receive an input data item and to compress the data item into an encoding comprising a plurality of numerical values. The numerical values are grouped at least according to whether they relate to content of the input data item or style of the input data item. The encoder has been trained using a plurality of groups of training data items grouped according to the content and where training data items within individual ones of the groups vary with respect to the style. The encoder has been trained using a training objective which takes into account the groups.

A data compression system includes a memory to store a plurality of predetermined prefixes corresponding to a plurality of classes of data. A classifying module is configured to receive data, receive a class of the data, and select a prefix to compress the data from the plurality of predetermined prefixes based on the data and the class of the data. A compressing module is configured to compress the data using the prefix. A header generating module is configured to generate a header including an indication of the prefix used to compress the data, and to output the header and the compressed data for storage or transmission. Using the prefix from the predetermined prefixes to compress the data eliminates an overhead of fetching the prefix from outside the data compression system.

A data encoding system includes a non-transitory memory, a processor, a digital-to-analog converter (DAC) and a transmitter. The non-transitory memory stores a predetermined file size threshold. The processor is in operable communication with the memory, and is configured to receive data. The processor detects a file size associated with the data. When the file size is below the predetermined file size threshold, the processor compresses the data using a variable length codeword (VLC) encoder. When the file size is not below the predetermined file size threshold, the processor compresses the data, using a hash table algorithm. The DAC is configured to receive a digital representation of the compressed data from the processor and convert the digital representation of the compressed data into an analog representation of the compressed data. The transmitter is coupled to the DAC and configured to transmit the analog representation of the compressed data.

A scalable hierarchical coreference method that employs a homomorphic compression scheme that supports addition and partial subtraction to more efficiently represent the data and the evolving intermediate results of probabilistic inference. The method may encode the features underlying conditional random field models of coreference resolution so that cosine similarities can be efficiently computed. The method may be applied to compressing features and intermediate inference results for conditional random fields. The method may allow compressed representations to be added and subtracted in a way that preserves the cosine similarities.

One aspect of the disclosure provides a device, comprising: an allocation module, for determining one or more metrics of each of a plurality of data streams; a compression module, for compressing each of the plurality of data streams and generating a plurality of compressed data streams, the compression module applying a compression ratio that varies as a function of the metrics determined by the allocation module; and a buffer memory, for storing the plurality of compressed data streams.

Described herein is a system that facilitates recommending data compression using machine learning and statistical attributes. According to an embodiment, a system can comprise receiving a dataset, statistical attributes associated with the dataset, and a compression requirement for compression of the dataset. The system can further comprise based on the statistical attributes and the compression requirement, estimating a first compression attribute and a second compression attribute of a group of compression processes. The system can further comprise selecting a primary compression process from the group of compression processes, based on an output of an analytics component, wherein the analytics component employs a neural network to determine the primary compression process based on analysis of the statistical attributes, the compression requirement, and a compression objective.

A method of storing data is provided. The method includes receiving a first set of data provided over a network session, and compressing the first set of data to form a second set of data. As further provided, the second set of data includes a number of bytes smaller than the first set of data. Further, the second set of data includes a portion of compressed data that is common to other network sessions. The method further includes compressing further the portion of the compressed data common to other network sessions to obtain a third set of data, and storing the third set of data.

Method and devices for coding point cloud data using a planar coding mode. The planar coding mode may be signaled using in a planar mode flag to signal that a volume is planar. A planar volume has all of its occupied child nodes on one side of a plane bisecting the volume. A planar position flag may signal which side of the volume is occupied. Entropy coding may be used to code the planar mode flag and/or the planar position flag. Context determination for coding may take into account one or more of whether a parent volume containing the volume is planar in occupancy, occupancy of a neighbouring volume at a parent depth, distance between the volume and a closest already-coded occupied volume at a same depth as the volume, plane position, if any, of the closest already-coded occupied volume, and a position of the volume within the parent volume.

Embedded codec (EBC) circuitry for frequency-dependent coding of transform coefficients, groups a plurality of transform coefficients for an input image block into a plurality of groups of transform coefficients. The plurality of transform coefficients are grouped based on a frequency distribution of the plurality of transform coefficients for the input image block. The EBC circuitry selects a different entropy coding parameter from a set of entropy coding parameters for each group of the plurality of groups, based on the frequency distribution. Thereafter, the EBC circuitry applies an entropy coding scheme from a set of entropy coding schemes to each group of transform coefficients, in accordance with the selected entropy coding parameter.

The present disclosure includes analyzing client instance performance trends to predict future client instance performance and adjusting thresholds used to send resource utilization alerts based on analyzing the client instance performance trends. In particular, a data center providing a platform as a service includes a database that stores performance data associated with client instances. The data center also includes alignment logic that temporally aligns the performance data, and a frequency based filter that compresses the aligned performance data based on frequency of values. The data center further includes dynamic threshold adjustment logic that adjusts thresholds associated with sending performance trend alerts based on analyzing the compressed set of performance data. In this manner, the thresholds may be dynamically adjusted for changing circumstances and/or relevant details associated with resource usage, and thus may more accurately send performance trend alerts indicative of situations when resource utilization becomes high and resources become low.