Techniques are provided herein for more efficiently storing images that have a common subject, such as product images that share the same product in the image. Each image undergoes an adaptive tiling procedure to split the image into a plurality of tiles, with each tile identifying a region of the image having pixels with the same content. The tiles across multiple images can then be clustered together and those tiles having identical content are removed. Once all duplicate tiles have been removed from the set of all tiles across the images, the tiles are once again clustered based on their encoding scheme and certain encoding parameters. Tiles within each cluster are compressed using the best compression technique for the tiles in each corresponding cluster. By removing duplicative tile content between numerous images of the same subject, the total amount of data that needs to be stored is reduced.

A disclosed information handling system includes an edge device communicatively coupled to a cloud computing resource. The edge device is configured to respond to receiving, from an internet of things (IoT) unit, a numeric value for a parameter of interest by determining a compressed encoding for the numeric value in accordance with a non-lossless compression algorithm. The edge device transmits the compressed encoding of the numeric value to the cloud computing resource. The cloud computing resource includes a decoder communicatively coupled to the encoder and configured to respond to receiving the compressed encoding by generating a surrogate for the numeric value. The surrogate may be generated in accordance with a probability distribution applicable to the parameter of interest. The compression algorithm may be a clustering algorithm such as a k-means clustering algorithm.

An electronic apparatus is provided. The electronic apparatus includes a storage storing a matrix included in an artificial intelligence model, and a processor. The processor divides data included in at least a portion of the matrix by one of rows and columns of the matrix to form groups, clusters the groups into clusters based on data included in each of the groups, and quantizes data divided by the other one of rows and columns of the matrix among data included in each of the clusters.

Methods and devices for coding point clouds using direct coding mode to code coordinates of a point within a sub-volume associated with a current node instead of a pattern of occupancy for child nodes. When direct coding is applied to two or more points in the sub-volume, the points are ordered based on one of their respective coordinate values and pairwise coding of those coordinate values is carried out on a bit-by-bit basis. The pairwise coding includes coding whether the bits are the same and, if so, the bit value.

A method for compressing RDF tuples. The method including obtaining RDF tuples, obtaining a dictionary of indices, encoding for each RDF tuple the indices attributed to the subject and the object, grouping RDF tuples sharing the same predicate and for each group sorting the RDF tuples by considering the encoding of the subject and the object, and for each group of sorted RDF tuples, serializing the index of the shared predicate, serializing the encoding of the subject and the object of a first RDF tuple, and for each RDF tuple of the group of sorted RDF tuples subsequent to the first RDF tuple of the group, computing a difference between the encoding of the subject and the object of a current RDF tuple and the encoding of the subject and the object of a previous RDF tuple, and serializing the computed difference in a form of a variable-length integer.

A method, and the associated design, schema and techniques for processing digital data, whether random or not, through encoding and decoding losslessly and correctly for purposes of encryption/decryption or compression/decompression or both, including the use of Digital Lensing, Unlimited Code System, and other associated techniques. There is no assumption of or requirement for the digital information to be processed before processing.

Provided is a storage system that can store various types of and large amounts of sensor data while accurately compressing the sensor data without wasting storage resources. The storage system includes: a storage that records sensor data output from a plurality of sensors; a processor that controls recording of sensor data in the storage; and a memory that records parameters of the plurality of sensors. The processor reads parameters assigned to the sensors that output the sensor data from the memory, normalizes the sensor data based on the parameters, compresses the normalized sensor data, and records the compressed sensor data in the storage.

A method for selectively dropping out feature elements from a tensor is disclosed. The method includes generating a mask that has a plurality of mask elements arranged in a first order. A compressed mask is generated, which includes a plurality of compressed mask elements arranged in a second order that is different from the first order. For example, each mask element of the plurality of mask elements of the mask is compressed to generate a corresponding compressed mask element of the plurality of compressed mask elements of the compressed mask. Individual compressed mask element of the plurality of compressed mask elements is indicative of whether a corresponding feature element of the tensor output by a neural network layer is to be dropped out or retained. Feature elements are selectively dropped from the tensor, based on the compressed mask.

Methods, systems, and techniques for data compression. A cluster fingerprint of an uncompressed data block is determined to correspond to a cluster fingerprint of a base block stored in a base array. This determining involves looking up the cluster fingerprint of the first base block from the base array using the cluster fingerprint of the first uncompressed data block. The difference between the uncompressed data block and the base block is determined, and a compressed data block is encoded using this difference. The compressed data block is then stored in a data array.

The present disclosure relates to an image processing apparatus and a method that enable decoding of encoded data of an octree in various processing orders. The octree corresponding to point cloud data is encoded after the context is initialized for each layer of the octree. Further, a breadth-first order or a depth-first order is selected as the decoding order for the encoded data of the octree corresponding to point cloud data, and the encoded data is decoded in the selected decoding order. The present disclosure can be applied to an image processing apparatus, an electronic apparatus, an image processing method, a program, or the like, for example.