A method, system and program product includes examining elements of a first matrix in a sequential fashion. Values of the examined elements are determined. A corresponding bit of a first mask is set to a first value if a determined value is zero. A corresponding bit of a first mask is set to a second value if a determined value is non-zero. The non-zero values are packed in a first vector, wherein bits of at least the first mask determine operations on packed values.

An audio encoding apparatus capable of reducing the bit rate even if a codebook having a larger codebook number is selected in a split multi-rate lattice vector quantization is provided. Sub-vector determining unit (121) determines, in the spectrum of an input signal having been divided into a predetermined number of sub-vectors, a sub-vector using the largest number of bits. Positional information encoding unit (122) encodes the positional information of the determined sub-vector. Codebook indication value estimating unit (124) estimates a number of used bits for a codebook indication value of the largest number of used bits by use of the (N−1) other codebook indication values, and generates a number-of-used-bits estimation value. Difference calculating unit (125) calculates a difference by subtracting the number-of-used-bits estimation value from the actual value of the codebook indication value of the largest number of used bits. Difference encoding unit (126) encodes the difference information.

Techniques of data compression involve ordering the points of a point cloud according to distance along a space-filling curve. Advantageously, a space-filling curve has the property that points close in distance along the curve are close together in Euclidean space. Thus, differences between points ordered by distance along such a curve, e.g., a Hilbert curve, will be close. When the curve is fractal, i.e., self-similar at all levels, the differences will be small even when the points are very unevenly clustered throughout the point cloud. Such small differences will provide greatly improved compression to the resulting delta-encoded set of points.

A method for compressing IQ data for high speed transport link and an associated device. The method comprises: determining, based on dynamical statistical distribution of the IQ data, one or more parameters of a companding function for a nonlinear companding operation (S310); applying the companding function with the determined one or more parameters on the IQ data (S320); performing uniform quantization on the IQ data to generate compressed IQ data (S330); and transmitting the compressed IQ (S340). And a method for decompressing compressed IQ data for high speed transport link, and an associated device.

A method for coding data includes: representing a first data in digital form as digital information; transforming the digital information into a first Gaussian integer; determining a relationship between the first Gaussian integer and a second Gaussian integer; and deriving, based on the determined relationship, a key for converting between the first Gaussian integer and the second Gaussian integer so as to associate the first data with a second data represented by the second Gaussian integer. The present invention also provides a system and a non-transient computer readable medium for implementing the method.

An optical network includes a transmitting portion configured to (i) encode an input digitized sequence of data samples into a quantized sequence of data samples having a first number of digits per sample, (ii) map the quantized sequence of data samples into a compressed sequence of data samples having a second number of digits per sample, the second number being lower than the first number, and (iii) modulate the compressed sequence of data samples and transmit the modulated sequence over a digital optical link. The optical network further includes a receiving portion configured to (i) receive and demodulate the modulated sequence from the digital optical link, (ii) map the demodulated sequence from the second number of digits per sample into a decompressed sequence having the first number of digits per sample, and (iii) decode the decompressed sequence.

The current document is directed to a multi-stage metric-data compression method and subsystem for compressing metric data collected and stored within distributed computing systems to facilitate computer-system management and administration. In a described implementation, metric data is partitioned into constant metric data, low-variability metric data, and high-variability metric data. High-variability metric data is compressed by identifying a set of basis metrics, or independent metrics, with respect to which a remaining set of dependent metrics can be expressed using coefficient multipliers. The high-variability metric data can then be stored as a set of independent metrics and set of coefficients, along with a small amount of additional data.

An arithmetic encoder for encoding a plurality of symbols having symbol values is configured to derive an interval size information for an arithmetic encoding of one or more symbol values to be encoded based on a plurality of state variable values representing statistics of a plurality of previously encoded symbol values with different adaptation time constants. The arithmetic encoder is configured to map a first state variable value, or a scaled and/or rounded version thereof, using a lookup-table and to map a second state variable value, or a scaled and/or rounded version thereof using the lookup-table, in order to obtain the interval size information describing an interval size for the arithmetic encoding of one or more symbols to be encoded. Further arithmetic encoders, arithmetic decoders, video encoders, video decoder, methods for encoding, methods for decoding and computer programs are also disclosed which are based on the same concept and on other concepts.

A computing system can include one or more processors and one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the computing system to perform operations including obtaining model structure data indicative of a plurality of parameters of a machine-learned model; determining a codebook comprising a plurality of centroids, the plurality of centroids having a respective index of a plurality of indices indicative of an ordering of the codebook; determining a plurality of codes respective to the plurality of parameters, the plurality of codes respectively comprising a code index of the plurality of indices corresponding to a closest centroid of the plurality of centroids to a respective parameter of the plurality of parameters; and providing encoded data as an encoded representation of the plurality of parameters of the machine-learned model, the encoded data comprising the codebook and the plurality of codes.

Methods and apparatuses for fronthaul signal compression and decompression. An apparatus for fronthaul signal compression includes a receiver, signal processing circuitry, and a fronthaul interface. The receiver is configured to receive one or more signals comprising complex samples. The signal processing circuitry is configured to construct vectors representing at least a portion of the complex samples; map the vectors to codeword indices in a vector quantization codebook; and process the codeword indices into an output signal. The fronthaul interface is configured to transmit the output signal via a fronthaul communication link of a wireless network. The vectors may be constructed according to the selected vectorization method. The vector quantization codebook may be selected from a set of vector quantization codebooks generated based on training signals and signaled.