A data amount compressing method for compressing a data amount corresponding to a learned model obtained by letting the learning model learn a predetermined data group, the learning model having a tree structure in which multiple nodes associated with respective hierarchically divided state spaces are hierarchically arranged, wherein each node in the learned model is associated with an error amount that is generated in the process of the learning and corresponds to prediction accuracy, and the data amount compressing method includes: a reading step of reading the error amount associated with each node; and a node deleting step of deleting a part of the nodes of the learned model according to the error amount read in the reading step, thereby compressing the data amount corresponding to the learned model.

The present technology relates to a data processing apparatus, data processing method, and a program which improve a compression ratio. Provided are a first compression part compressing data using compressed sensing and a second compression part compressing observation coefficients coming from the first compression part using a method different from the method used in the first compression part. The first compression part performs random sampling using a sampling matrix optimized for compression by the second compression part. The sampling matrix is designed to minimize the differential value between the observation coefficients. The present technology can be applied to recording/reproducing apparatuses that capture, compress, record, and reproduce image data.

The disclosure provided a compressing method of the grayscale compensation table of an OLED display panel, which comprising: step 10, when transmitting a set of grayscale compensation table of the OLED display panel to an encoder for encoding, firstly, performing a differential calculation on many grayscale compensation tables with a same color channel and different gray scales in the set of which to acquire a corresponding reference image and a difference image as replacements of many grayscale compensation tables; step 20, transmitting the above images to the encoder; step 30, the encoder compressing and encoding a received grayscale compensation table. The compressing method of the grayscale compensation table of the OLED display panel performs the intra-level differences between the same color component and the different grayscale compensation tables in the same OLED compensation table to improve an efficiency and a performance of the compression compensation table.

A receiver includes: a filter circuit to generate predicted measurements for a set of tracks based on previous kinematic states of the tracks and timing and source data of next compressed measurements associated to the tracks, generate probability data of differences between the predicted measurements and next measurement data, generate the next measurement data using the predicted measurements and quantized differences between the predicted measurements and the next measurement data, and generate next kinematic states of the tracks based on the previous kinematic states, the timing and source data of the next compressed measurements, and the generated measurement data; a quantizer circuit to quantize the probability data into quantization tables and look up the quantized differences from corresponding indices in the quantization tables; and a decoder circuit to decode encoded index data of the next compressed measurements into the corresponding indices in the quantization tables using the quantized probability data.

Methods, devices and systems enhance compression and decompression of data values when they comprise a plurality of semantically meaningful data fields. Compression is sometimes not applied to each data value as a whole, but instead to at least one of the semantically meaningful data fields of each data value, and in isolation from the other ones. Data fields can be organized that share the same semantic meaning together to accelerate compression and decompression as multiple compressors and decompressors can be used in parallel. A system can be used where methods and devices are tailored to perform compression and decompression of the semantically meaningful data fields of floating-point numbers after first partitioning further at least one of said data fields into two or a plurality of sub-fields to increase the degree of value locality and improve compressibility of floating-point values.

A device (100) for and method of determining clusters of sequences of instances of a first type of data for compacting a data set comprising sequences of instances of the first type of data is provided. Also a method of compacting a data set, a method of transmitting compacted data and a computer program product are provided. In a sequence clustering unit (110) of the device, sequences of a first set of data are clustered on basis of conditional probabilities. Each unique sequence of the first set of data is associated with one or more conditional probabilities that an instance of the second set of data has a specific value given the unique sequence. In the clustering a significant part of the mutual information between the first set of data and the second set of data is maintained.

A data reduction device (150) for and a method of reducing a data set based on a subset of variables from a set of variables are provided. Instances of the plurality of variables comprise information to predict an instance of a further type of data. The device comprises a first data set unit (102), a second data set unit (104), a searching unit (110) and a data reduction unit (152). The first data set unit obtains a first set comprising tuples of instances of data. The second data set unit obtains a second set comprising instances of the further type of data. Each instance of the second set corresponds to one of the tuples of the first set. The searching unit obtains a reduced set of variables that represents an at least local optimum of an optimization function being a combination of a first mutual information value between the reduced first set and the second set and a penalty value being based on a number of variables in the reduced set of variables.

The entropy coding of a current part of a predetermined entropy slice is based on, not only, the respective probability estimations of the predetermined entropy slice as adapted using the previously coded part of the predetermined entropy slice, but also probability estimations as used in the entropy coding of a spatially neighboring, in entropy slice order preceding entropy slice at a neighboring part thereof. Thereby, the probability estimations used in entropy coding are adapted to the actual symbol statistics more closely, thereby lowering the coding efficiency decrease normally caused by lower-delay concepts. Temporal interrelationships are exploited additionally or alternatively.

Some disclosed embodiments are directed to methods and systems for performing pair-wise delta compression. For example, systems obtain a set of files to be compressed into a single compressed file. The system identifies different attributes related to the set of files. For each file in the set of files, the system predicts an optimized set of candidate compression files and calculates a delta between each file in the optimized set and the target file corresponding to the optimized set. After identifying the smallest delta, the system compresses the selected pair of files associated with the smallest delta in order to generate the single compressed file for the set of files.

The entropy coding of a current part of a predetermined entropy slice is based on, not only, the respective probability estimations of the predetermined entropy slice as adapted using the previously coded part of the predetermined entropy slice, but also probability estimations as used in the entropy coding of a spatially neighboring, in entropy slice order preceding entropy slice at a neighboring part thereof. Thereby, the probability estimations used in entropy coding are adapted to the actual symbol statistics more closely, thereby lowering the coding efficiency decrease normally caused by lower-delay concepts. Temporal interrelationships are exploited additionally or alternatively.