A method for lossy data compression, the method including receiving raw data at a storage device, receiving a request to compress flag, accessing an onboard data compression algorithm library containing various data compression algorithms respectively corresponding to lossy data compression schemes, selecting one of the data compression algorithms based on a number of parameters, running the selected data compression algorithm either online such that the raw data is compressed by the storage device when it is received, and is then stored on the storage device as compressed data, or offline such that the raw data is stored at the storage device, is later compressed by the storage device according to the selected data compression algorithm, and is resaved at the storage device as compressed data.

Methods, apparatuses, and storage media associated with compaction of data from one or more computing devices are disclosed. In various embodiments, one or more Internet of Things (IoT) devices may transmit information to a computing system. The computing system may group together raw data received from these one or more IoT devices based on a shared attribute. The computing system may select a compaction scheme to represent the knowledge conveyed by a group of the raw data. The computing system may apply this compaction scheme to the group of raw data to generate data that is representative of the group of raw data. Other embodiments may be disclosed or claimed.

A scalable vehicle data compression system includes one or more processors, one or more memory modules communicatively coupled to the one or more processors and machine readable instructions stored in the one or more memory modules and upon execution by the one or more processors, performing at least (i) analyzing a data stream, (ii) determining a type of the data stream, an estimated usage of a network bandwidth, a current processing load of the processor, or a combination thereof, (iii) determining a level of compression based on the data type, the estimated usage of the network bandwidth, the current processing load of the processor, or a combination thereof, and (iv) compressing the data stream with the processor based on the determined level of compression.

Data compression techniques are provided that remove redundancy across the boundary of compression search engines. An illustrative method comprises splitting the data frame into a plurality of sub-chunks; comparing at least two of the plurality of sub-chunks to one another to remove at least one sub-chunk from the plurality of sub-chunks that substantially matches at least one other sub-chunk to generate a remaining plurality of sub-chunks; generating matching sub-chunk information for data reconstruction identifying the at least one removed sub-chunk and the corresponding substantially matched at least one other sub-chunk; grouping the remaining plurality of sub-chunks into sub-units; removing substantially repeated patterns within the sub-units to generate corresponding compressed sub-units; and combining the compressed sub-units with the matching sub-chunk information to generate a compressed data frame. The data frame optionally comprises one or more host pages compressed substantially simultaneously, and the compressed data frame for a plurality of host pages compressed substantially simultaneously comprises a host page address for each host page.

Methods, devices and systems enhance compression and decompression of data blocks of data values by selecting the best suited compression method and device among two or a plurality of compression methods and devices, which are combined together and which said compression methods and devices compress effectively data values of particular data types; said best suited compression method and device is selected using as main selection criterion the dominating data type in a data block by predicting the data types within said data block.

A data compression system comprising computer memory to store plural compression algorithms and a hardware processor to apply compression algorithm/s to incoming data items, wherein the compression algorithm to be applied to individual data item/s from among the incoming data items is selected, from among the plural compression algorithms, by the hardware processor, depending at least on the individual data item.

Embodiments of the present disclosure provide systems, methods, and computer storage media for optimizing computing resources generally associated with cloud-based media services. Instead of decoding digital assets on-premises to stream to a remote client device, an encoded asset can be streamed to the remote client device. A codebook employable for decoding the encoded asset can be embedded into the stream transmitted to the remote client device, so that the remote client device can extract the embedded codebook, and employ the extracted codebook to decode the encoded asset locally. In this way, not only are processing resources associated with on-premises decoding eliminated, but on-premises storage of codebooks can be significantly reduced, while expensive bandwidth is freed up by virtue of transmitting a smaller quantity of data from the cloud to the remote client device.

A data encoder including a preprocessor configured to divide a data stream into a plurality of sub data blocks; a plurality of meta data generators each configured to generate meta data from one of the plurality of sub data blocks; and a plurality of data compressors each configured to compress one of the plurality of sub data blocks according to the meta data.

Methods, devices and systems enhance compression and decompression of data blocks of data values by selecting the best suited compression method and device among two or a plurality of compression methods and devices, which are combined together and which said compression methods and devices compress effectively data values of particular data types; said best suited compression method and device is selected using as main selection criterion the dominating data type in a data block by predicting the data types within said data block.

A system is disclosed comprising a transceiver, transcoder, memory, and a processor for receiving raw data, partitioning the raw data into substrings of predetermined length, assigning each substring to a corresponding predetermined frequency based upon a data set or first lookup table based on the substring's given pattern, and transmitting said frequency using an antenna. Embodiments include a compression component for receiving raw data as input, breaking the raw data into subsets of predetermined length, comparing the raw data to a second lookup table, the second lookup table comprising all possible bit patterns for a file of the length of the raw data, wherein the possible bit patterns are partitioned in n-bit partitions, the n-bit partitions having a corresponding assigned value, the values of which are assembled by a given function so as to produce a code for each possible bit pattern.