An enhanced graph transformation-based point cloud attribute compression method. For point cloud attribute information, a point cloud is first subjected to airspace division by using a K-dimension (KD) tree; a new graph transformation processing method in combination with spectral analysis is provided; the point cloud is then subjected to spectral clustering on graphs in coded blocks of the point cloud; expansion is performed on the basis of existing graph transformation to implement a local graph transformation scheme; enhanced graph transformation with two transformation modes is formed; the compression performance of graph transformation is improved. The method comprises: performing color space transformation of point cloud attributes; dividing the point cloud by using the KD tree to obtain the coded blocks; performing spectral clustering-based enhanced graph transformation; performing transformation mode decision; and performing uniform quantization and entropy coding. Provided is a new spectral analysis-based enhanced graph transformation scheme, wherein two transformation modes are comprised, and the optimal mode is selected by the mode decision; after the point cloud is divided with the tree, a graph is created in each coded block and the graph transformation is used as transformation mode I; on this basis, graph spectral clustering is implemented; the graph is divided into two local graphs and then local graph transformation is performed to serve as transformation mode II; in the enhanced graph transformation scheme supporting the two transformation modes, the optimal mode is selected by the mode decision to achieve the optimal performance of point cloud attribute compression.

A system and method for fast communication between processors on complex chips using encoding, wherein a training data set is used to find patterns and associated smaller indices, or codewords, which are stored in a reference codebook library, and where reconstruction and deconstruction algorithms are used to encode and decode data as it is received. The codebook and algorithms may be stored in the firmware of a semiconductor which enable reduced resources and cost when transmitting data between or among devices that utilize such semiconductors.

A method for generating codewords, the method may include obtaining an input tree, converting the input tree to a modified tree in which each symbol score is a power of two, wherein the conversion is responsive to a relationship between the aggregate count of symbols of the input tree and to a certain power to two that is a smallest power of two that exceeds the aggregate count of symbols; and assigning a codeword to each symbol based at least on the symbol counts associated with the leaves of the modified tree.

The described technology is generally directed towards reducing the amount of data stored in a sequence of data blocks by combining deduplication and compression. According to an embodiment, a system can comprise a memory that can store computer executable components, and a processor that can execute the components stored in the memory. The components can comprise a data block identifier that can identify, for a sequence of data blocks, a first data block that corresponds to a first data, resulting in a first identified data block, and a deduplication component that can identify a second data block that corresponds to the first data, resulting in a second identified data block, wherein the deduplication component can replace the second identified data block with a key value corresponding to the first identified data block. Further, a compression component can compress the first identified data block, resulting in a compressed data block.

An example method of compressing a data set includes determining whether individual values from a data set correspond to a first category or a second category of values. Based on one of the values corresponding to the first category, the value is added to a compressed data set. Based on one of the values corresponding to the second category, the value is excluded from the compressed data set, and a statistical distribution of values of the second category is updated based on the value. During a first phase, the determining is performed for a plurality of values from a first portion of the data set based on comparison of the values to criteria. During a second phase, the determining is performed for a plurality of values from a second portion of the data set based on the statistical distribution.

Disclosed are techniques for encoding a set of sensor content symbols at least in part via applying a processing window of an adjustable size.

A method of near-lossless universal data compression using correlated data sequences includes detecting first target surroundings via a first sensor, encoding a first data sequence indicative of the detected target surroundings, and communicating to an electronic controller, the encoded first data sequence. The method additionally includes detecting the first target surroundings via a second sensor, and encoding a second data sequence indicative of the target surroundings detected by the second sensor. The method also includes communicating the encoded second data sequence to the controller. The method additionally includes decoding, via the controller, the encoded first and second data sequences. The method also includes, via the controller, determining a statistical correlation between the decoded first and second data sequences and formulating a mapping function having reduced cardinality and indicative of the determined statistical correlation. Furthermore, the method includes feeding back the mapping function by the controller to the first processor.

A system and method for encoding data using a plurality of encoding libraries. Portions of the data are encoded by different encoding libraries, depending on which library provides the greatest compaction for a given portion of the data. This methodology not only provides substantial improvements in data compaction over use of a single data compaction algorithm with the highest average compaction, but provides substantial additional security in that multiple decoding libraries must be used to decode the data. In some embodiments, each portion of data may further be encoded using different sourceblock sizes, providing further security enhancements as decoding requires multiple decoding libraries and knowledge of the sourceblock size used for each portion of the data. In some embodiments, encoding libraries may be randomly or pseudo-randomly rotated to provide additional security.

A matrix data processing method performed by a computing device which performs a matrix multiplication operation includes, with respect to each of one or more elements included in a matrix, when a value of each element satisfies a designated condition, determining the element to be a don't-care element and determining an output value of the don't-care element, generating a bitstream based on the output value of the don't-care element and index values of valid elements included in the matrix, and equally dividing the bitstream into pieces of a designated number, and generating a Huffman code corresponding to each of a plurality of lower bitstreams that are generated as a result of the equal division.

A method includes obtaining an array of sorted identifiers to be stored in a designated portion of a memory of a given computing system, determining a segment size for splitting elements of the array into a plurality of segments, splitting the array into the plurality of segments based at least in part on the determined segment size, and compressing the plurality of segments to create a plurality of compressed segments. The method also includes generating a balanced binary search tree comprising a plurality of nodes each identifying a range of elements of the array corresponding to a given one of the segments and comprising a pointer to a given compressed segment corresponding to the given segment. The method further includes maintaining the balanced binary search tree and the compressed segments in the designated portion of the memory, and processing queries to the array utilizing the balanced binary search tree.