A method for text compression comprises recognizing a prefix string of one or more text characters preceding a target string of a plurality of text characters to be compressed. The prefix string is provided to a natural language generation (NLG) model configured to output one or more predicted continuations each having an associated rank. If the one or more predicted continuations include a matching predicted continuation relative to the next one or more text characters of the target string, the next one or more text characters are compressed as an NLG-type compressed representation. If no predicted continuations match the next one or more text characters of the target string, a longest matching entry in a compression dictionary is identified. The next one or more text characters of the target string are compressed as a dictionary-type compressed representation that includes the dictionary index value of the longest matching entry.

Techniques and systems are provided for compressing data in a neural network. For example, output data can be obtained from a node of the neural network. Re-arranged output data having a re-arranged scanning pattern can be generated. The re-arranged output data can be generated by re-arranging the output data into the re-arranged scanning pattern. One or more residual values can be determined for the re-arranged output data by applying a prediction mode to the re-arranged output data. The one or more residual values can then be compressed using a coding mode.

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 method, computer program, and computer system is provided for point cloud coding. The method includes receiving, from a bitstream, data corresponding to a point cloud; reconstructing, based on the data, a first attribute value of a first duplicate point from among a plurality of duplicate points corresponding to a single geometry position; obtaining at least one prediction residual corresponding to at least one remaining attribute value of at least one remaining duplicate point from among the plurality of duplicate points; reconstructing the at least one remaining attribute value based on the reconstructed first attribute and the at least one prediction residual; and decoding the data corresponding to the point cloud based on the reconstructed first attribute value and the reconstructed at least one remaining attribute value.

A method for compressing Demura compensation value including: acquiring original compensation values of a target panel; inputting the original compensation values into a spatial domain sampling model, performing a down-sampling to obtain spatial domain sampling values, inputting the original compensation values into a prediction mode overall model, and performing a numerical processing to obtain a plurality of mode characteristic values; performing a data reconstruction on the spatial sampling values and the plurality of mode characteristic values to obtain a reconstructed value set; performing a syntactic encoding on the spatial sampling values and the plurality of mode characteristic values to obtain a syntactic element code set; performing a mode selection according to the reconstructed value set and the syntactic element code set to obtain an optimal prediction mode; and acquiring and outputting a syntactic element code corresponding to the optimal prediction mode to obtain a Demura compensation value compression code.

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 predictive model utilizes a set of coefficients for processing received input data. To reduce memory usage storing the coefficients, a compression circuit compresses the set of coefficients prior to storage by generating a cumulative count distribution of the coefficient values, and identifying a distribution function approximating the cumulative count distribution. Function parameters for the determined function are stored in a memory and used by a decompression circuit to apply the function the compressed coefficients to determine the decompressed component values. Storing the function parameters may consume less memory in comparison to storing a look-up table for decompression, and may reduce an amount of memory look-ups required during decompression.

The invention relates to a method for compressing a set of input binary data values x, all coded in a same number B of bits, into a corresponding set of output data values x, all coded in a smaller number b of bits, obtainable by (i) computing a quantization step size dq $\mathrm{dq}=\frac{2^{B+1}}{2^{b+1}-1}$ (ii) dividing the range of values [0, 2.sup.B1[, into 2.sup.b subranges, the first subrange being [0, INT(dq/2)[ the i.sup.th subrange being [INT((i3/2)*dq)+1, INT((i1/2)*dq)[, for i going from 2 to 2.sup.b The invention also relates to a method for decompressing data and to applications of said method for compressing/decompressing video data, and to devices for performing these operations.

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.

A method for compressing Demura compensation value including: acquiring original compensation values of a target panel; inputting the original compensation values into a spatial domain sampling model, performing a down-sampling to obtain spatial domain sampling values, inputting the original compensation values into a prediction mode overall model, and performing a numerical processing to obtain a plurality of mode characteristic values; performing a data reconstruction on the spatial sampling values and the plurality of mode characteristic values to obtain a reconstructed value set; performing a syntactic encoding on the spatial sampling values and the plurality of mode characteristic values to obtain a syntactic element code set; performing a mode selection according to the reconstructed value set and the syntactic element code set to obtain an optimal prediction mode; and acquiring and outputting a syntactic element code corresponding to the optimal prediction mode to obtain a Demura compensation value compression code.