A processing device is provided which includes a plurality of encoders each configured to compress a portion of data using a different compression algorithm. The processing device also includes one or more processors configured to cause an encoder, of the plurality of encoders, to compress the portion of data when it is determined that the portion of data, which is compressed by another encoder configured to compress the portion of data prior to the encoder in an encoder hierarchy, is not successfully compressed according to a compression metric by the other encoder in the encoder hierarchy. The one or more processors are also configured to prevent the encoder from compressing the portion of data when it is determined that the portion of data is successfully compressed according to the compression metric by the other encoder in the encoder hierarchy.

Apparatus comprises data compression circuitry to process a set of data values, the data compression circuitry comprising: detector circuitry to detect, for each of n complementary groups of m data values of the set of data values, a first subset of the groups for which the data values in the group have a predetermined pattern of data values, where m and n are integers and mn is the number of data values in the set of data values; generator circuitry to generate a compressed data packet comprising at least: a representation of a second subset of the groups, the second subset being each of then complementary groups other than groups in the first subset; and an indication of a group position, with respect to the set of data values, of each group in the second subset of groups. Complementary decompression apparatus is also described.

Compressed domain processors configured to perform operations on data compressed in a format that preserves order. The Compressed domain processors may include operations such as addition, subtraction, multiplication, division, sorting, and searching. In some cases, compression engines for compressing the data into the desired formats are provided.

A system, method and computer program product for encoding an input string of binary characters representing alphanumeric characters. A system includes: a character writing engine for writing a binary character to an empty cell of a multi-dimensional shape beginning with a starting empty cell; a next cell determination engine for determining a next empty cell by traversing neighboring cells in the multi-dimensional shape until an empty cell is located; a loop facilitator for looping back to the character writing engine and the next cell determining engine until no more data characters or a next empty cell is not determined; and a serialization engine for serializing the cells into a one dimensional binary string of characters representing an encoded string of alphanumeric characters.

Embodiments of the present disclosure provide a method, an apparatus, and a system for deep feature coding and decoding. The method comprises: extracting features of respective video frames; determining types of the features, the types reflecting time-domain correlation degrees between the features and a reference feature; encoding the features using predetermined coding patterns matching the types to obtain coded features; and transmitting the coded features to the server such that the server decodes the coded features for a vision analysis task. By using the embodiments of the present disclosure, videos per se may not be transmitted to the cloud server; instead, the features of the video, after being encoded, are transmitted to the cloud server for a vision analysis task; compared with the prior art, data transmission pressure may be lowered, and the storage pressure at the cloud server may also be lowered.

Technologies for computing rolling hashes include a computing device having a first hash table that includes a first plurality of random-valued entries and a second hash table that includes a second plurality of random-valued entries. The computing device retrieves a block of data from a data buffer and generates a hash based on the block of data, a previously generated hash, the first hash table, and the second hash table. The computing device further determines whether the generated hash matches a predefined trigger and records a data boundary in response to a determination that the generated hash matches the trigger.

Disclosed herein are an apparatus and method for data compression and decompression based on calculation of an error vector magnitude. A data compression apparatus includes a lossy compression unit for calculating, in advance, a reference error vector magnitude depending on lossy compression and decompression by removing lower bits from a data signal composed of bitstreams, for setting removal target lower bits satisfying a preset error vector magnitude requirement, and for lossily compressing the data signal by removing the removal target lower bits from the data signal, and a communication unit for transmitting the compressed data signal to a data decompression apparatus.

Mechanisms are disclosed for performing efficient lossless encryption and decryption to reduce power consumption and improve the efficiency of processing streams of digital data. An input data stream associated with a stream of input data is received. A discrete wavelet transform is applied to a first serial input data value in the input data stream. Distributed components of the first serial input data value are distributed by computing a cumulative probability that the first serial input data value is less than or equal to a power-of-two range value associated with a power-of-two probability distribution function. An entropy encoded encryption value is computed based on a range variant asymmetrical numeral system based on the power-of-two probability distribution function. The entropy encoded encryption value is unloaded based on one or more computed next states. Compressed serial output data streams are compressed based on the unloaded entropy encoded encryption value.

Techniques for writing data to a subset of memory devices are described. In one aspect, a block of data to be written to a line in a rank of memory may be received. The rank of memory may comprise a set of memory devices. The block of data may be compressed. The compressed block of data may be written to a subset of the memory devices that comprise the line. The unwritten portions of the line may not be used to store valid data.

Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.