A method of encoding image data, including: frequency-transforming input image data to generate an array of frequency-transformed input image coefficients by a matrix-multiplication process, according to a maximum dynamic range of the transformed data and using transform matrices having a data precision; and selecting the maximum dynamic range and/or the data precision of the transform matrices according to the bit depth of the input image data.

A system and method for data compression with intrusion detection, that measures in real-time the probability distribution of an encoded data stream, compares the probability distribution to a reference probability distribution, and uses one or more statistical algorithms to determine the divergence between the two sets of probability distributions to determine if an unusual distribution is the result of a data intrusion. The system comprises both encoding and decoding machines, an intrusion detection module, a codebook training module, and various databases which perform various analyses on encoded data streams.

In one embodiment, an apparatus comprises a memory; and a compression engine comprising circuitry, the compression engine to assign weights to a plurality of first symbols of a data set, a weight representing a frequency of a corresponding first symbol in the data set; perform a partial sort of the first symbols based on the assigned weights; generate at least a portion of a Huffman tree based on the partial sort; and create a plurality of Huffman codes for the plurality of first symbols based on the Huffman tree.

An encoder is provided. The encoder is configured to analyze input data to identify at least one mode symbol therein. The encoder is configured to generate data values of a first type including non-mode symbols and data values of a second type including runs of the at least one mode symbol. Moreover, the encoder is configured to generate information that is indicative of a count of the non-mode symbols and information that is indicative of the at least one mode symbol. Furthermore, the encoder is configured to assemble or encode the information that is indicative of the at least one mode symbol, the information that is indicative of the count of the non-mode symbols, the data values of the first type including the non-mode symbols and the data values of the second type including the runs of the at least one mode symbol, to generate encoded data.

Methods and apparatus are described by which data is compressed using semi-dynamic Huffman code generation. Embodiments generate symbol statistics over a portion of data. The symbol statistics are expanded to include all possible literals that could appear within the data. Any literal or reference added to the statistics may be given a frequency of one. The statistics are used to generate a semi-dynamic Huffman code. The entire data is then compressed using the semi-dynamic Huffman code.

Systems for data compression using content estimation. In one aspect, a system includes a dictionary encoder and one or more additional data compression encoders. One or more processors are configured to determine compression statistics related to a first data block, estimate a size of a first compressed data block, estimate a size of a second compressed data block, compare the estimated sizes, compress the first data block using the dictionary encoder when the estimated size of the first compressed data block is smaller than the estimated size of the second compressed data block, and compress the first data block using the additional data compression encoder when the estimated size of the second compressed data block is smaller than the estimated size of the first compressed data block.

Methods and apparatuses are provided to approximate a cumulative distribution function (CDF) interval-wise with second order polynomials, while posing constraints on the polynomials within the intervals and/or on the boundary between the intervals. In this manner, a CDF approximation is obtained, which may be used in a variety of applications including entropy encoding and decoding of any source data. The constraints correspond to the characteristics of the CDF to be approximated.

An encoder is provided. The encoder is operable to analyze input data (D1) to identify at least one mode symbol therein. The encoder is operable to generate data values of a first type including non-mode symbols and data values of a second type including runs of the at least one mode symbol. Moreover, the encoder is operable to generate information that is indicative of a count of the non-mode symbols and information that is indicative of the at least one mode symbol. Furthermore, the encoder is operable to assemble or encode the information that is indicative of the at least one mode symbol, the information that is indicative of the count of the non-mode symbols, the data values of the first type including the non-mode symbols and the data values of the second type including the runs of the at least one mode symbol, to generate encoded data (E2).

A method for encoding and decoding, an encoder and decoder for a point cloud. The method for encoding a point cloud to generate a bitstream of compressed point cloud data, in which the point cloud's geometry is represented by an octree-based structure with a plurality of nodes having parent-child relationships by recursively splitting a volumetric space containing the point cloud into sub-volumes each associated with a node of the octree-based structure, includes: determining an occupancy pattern for a parent node based on the occupancy of its child nodes; determining a planar context information for at least one of the child nodes; and entropy encoding/decoding the occupancy pattern parent node based on the determined planar context information to produce encoded/decoded data for the bitstream.

A system and method for lossy precompression for data compaction using automated model monitoring and training, wherein statistical analyses of test datasets are used to determine if the probability distribution of two datasets are within a pre-determined range, and responsive to that determination new encoding and decoding algorithms may be retrained in order to produce new data sourceblocks, and pre-compression of data prior to processing and statistical analysis allows for the compaction of already compressed data into highly dense formats. The new data sourceblocks may then be processed and assigned new codewords which are compiled into an updated codebook which may be distributed back to encoding and decoding systems and devices.