Abstract: Methods and systems for processing telemetry data that contains multiple data types is disclosed. Optimum multimodal encoding approaches can be used which can achieve data-specific compression performance for heterogeneous datasets by distinguishing data types and their characteristics at real-time and applying most effective compression method to a given data type. Using an optimum encoding diagram for heterogeneous data, a data classification algorithm classifies input data blocks into predefined categories, such as Unicode, telemetry, RCS and IR for telemetry datasets, and a class of unknown which includes non-studied data types, and then assigns them into corresponding compression models.

Abstract: A disclosure for lossless data compression can include receiving a data block by a processor, performing, by the processor, a sparse transform extraction on the data block, selecting, by the processor, a transform matrix for the data block, modeling, by the processor, the selected transform matrix for the data block, selecting, by the processor, a transform coefficient model for the data block, modeling, by the processor, the selected transform coefficient model for the data block, compressing, by the processor, the data in the data block using the selected transform matrix and the selected transform coefficient model.

Abstract: Methods and systems for processing telemetry data that contains multiple data types is disclosed. Optimum multimodel encoding approaches can be used which can achieve data-specific compression performance for heterogeneous datasets by distinguishing data types and their characteristics at real-time and applying most effective compression method to a given data type. Using an optimum encoding diagram for heterogeneous data, a data classification algorithm classifies input data blocks into predefined categories, such as Unicode, telemetry, RCS and IR for telemetry datasets, and a class of unknown which includes non-studied data types, and then assigns them into corresponding compression models.

Abstract: A method for compressing data is disclosed. The method may include receiving data from one or more data sources. The method may also include selectively classifying the data into one or more data streams, the one or more data streams including at least PCM-encoded data and image data. The method may further include separately compressing the PCM-encoded data and the image data into first and second compressed bit-streams. The method may also include shuffling the first and second compressed bit-streams.

Abstract: A method for compressing data is disclosed. The method may include receiving data from one or more data sources. The method may also include selectively classifying the data into one or more data streams, the one or more data streams including at least PCM-encoded data and image data. The method may further include separately compressing the PCM-encoded data and the image data into first and second compressed bit-streams. The method may also include shuffling the first and second compressed bit-streams.

Abstract: A method for compressing data is disclosed. The method may include classifying the data from a plurality of data sources into classifications including at least PCM-encoded data and data structure information. The method may also include determining data states associated with the PCM-encoded data and the data structure information. The method may further include compressing the PCM-encoded data and the data structure information into compressed bit-streams based on the determined data states. The method may also include shuffling the compressed bit-streams to introduce randomness.