Provided is a neural network device including at least one processor configured to implement an arithmetic circuit configured to generate third data including a plurality of pixels based on a neural network configured to perform an arithmetic operation on first data and second data, and a compressor configured to generate compressed data by compressing the third data, wherein the compressor is further configured to generate, as the compressed data, bitmap data comprising location information about a non-zero pixel having a non-zero data value among the plurality of pixels based on a quad-tree structure.

A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. A processing/filtering element utilizes occupancy map information and/or auxiliary patch information to determine relationships between patches in image frames and adjusts encoding/decoding and/or filtering or pre/post-processing parameters based on the determined relationships.

Techniques are disclosed relating to compression of pixel data using different quantization for different regions of a block of pixels being compressed. In some embodiments, compression circuitry is configured to determine, for multiple components included in pixels of the block of pixels being compressed, respective smallest and greatest component values in respective regions of the block of pixels. The compression circuitry may determine, based on the determined smallest and greatest component values, to use a first number of bits to represent delta values relative to a base value for a first component in a first region and a second, different number of bits to represent delta values relative to a base value for a second component in the first region. The compression circuitry may then quantize delta values for the first and second components of pixels in the first region of the block of pixels using the determined first and second numbers of bits. In some embodiments, the compression circuitry determines whether to provide cross-component bit sharing within a region.

Systems and methods for redeeming digital files are disclosed. In particular, the systems and methods relate to localized sharing of digital files such that the digital file is degraded when the file is redeemed. The digital file can include a plurality of bits, and bits of the digital file can be removed upon each transfer and/or access of the digital file. When a quantity of bits in the digital file falls below a predetermined threshold, the digital file can be deactivated. The systems can include an application that degrade the digital file. The degradation can include file compression, bitrate reduction, and/or removal of parity bits from the digital file. Security measures, such as private/public encryption keys, are also disclosed herein.

To provide a data compression/decompression system that can appropriately decompress compressed data. A first computer generates first identification information that can identify a first execution environment; and stores the first identification information on a storage device in association with compressed data of data. A second computer generates second identification information that can identify a second execution environment; and determines whether or not the first identification information, and the second identification information match. In a case that it is determined that the first identification information, and the second identification information do not match, the second computer requests a third computer that is capable of decompression of the compressed data to decompress the compressed data, the third computer decompresses the compressed data, and transmits decompressed data to the second computer, and the second computer completes the decompression of the compressed data by receiving the decompressed data from the third computer.

An apparatus, method and computer program is described comprising: degrading an acquired data signal, using a source coding module, to generate a degraded signal having a fidelity dependent on a first measure of available energy, wherein the acquired data signal is degraded based on a scalar dependent on said first measure of available energy; and generating an output based on the degraded data signal, wherein the output is generated using an inference module that has parameters dependent on a second measure of available energy, wherein the inference module is configured to output degradable inferences dependent on the degraded signal received by the inference module from the source coding module.

A method for processing data sets having at least one time series. These are measurement values of sensors that are sensed at certain times. The data of the measured values and the respective times at which the data were sensed are stored as data elements of the time series. The method compresses the data set by rounding the sensed data with subsequent decimation of the data elements of the data set which are contained in the time series.

A deep neural network (“DNN”) module can compress and decompress neuron-generated activation data to reduce the utilization of memory bus bandwidth. The compression unit can receive an uncompressed chunk of data generated by a neuron in the DNN module. The compression unit generates a mask portion and a data portion of a compressed output chunk. The mask portion encodes the presence and location of the zero and non-zero bytes in the uncompressed chunk of data. The data portion stores truncated non-zero bytes from the uncompressed chunk of data. A decompression unit can receive a compressed chunk of data from memory in the DNN processor or memory of an application host. The decompression unit decompresses the compressed chunk of data using the mask portion and the data portion. This can reduce memory bus utilization, allow a DNN module to complete processing operations more quickly, and reduce power consumption.

Aspects of the disclosure provide methods, apparatuses, and a non-transitory computer-readable medium for point cloud compression and decompression. In a method, syntax information of a point cloud in a quantized space is decoded from a coded bitstream. The syntax information includes dividing information and adaptive geometry quantization information for a bounding box of the point cloud. The bounding box of the point cloud is divided into a plurality of parts based on the dividing information. Quantization parameters for the parts in the bounding box are determined based on the adaptive geometry quantization information. Points in each of the parts in the bounding box of the point cloud are reconstructed based on the quantization parameter for the respective part in the bounding box.

A communication compression method based on model weight distribution in federated learning, and belongs to the technical field of wireless communication. Based on the existing federated average idea in federated learning, counts the distribution of model weight information to be transmitted between nodes during each communication, then performs scalar quantization and compression through Lloyd-Max quantizer according to their distribution characteristics, then encodes with Huffman coding method, and finally sends the codes to the target node, thereby the minimum mean square quantization error is realized and the number of bits required for communication is reduced.