An active optical compressive sensing system includes an optical source to generate light for illuminating a target and a pattern generator to generate a pattern. A pattern controller controls an operation of the pattern generator to cause generation of a desired pattern. The pattern is a spatial filtering pattern that enables data compression by generating sparse scattered data. Applying the pattern allows a logarithmic resource scaling.

The present invention suppresses the data size of a data frame to be transmitted to a control device at every control period even if oversampling is performed. A counter unit (10) compresses the data size of sampling data (Sd) indicating a second or subsequent count value (Ct) to the number of bits by which the maximum (Vmax) of a count value countable in one sampling processing can be represented.

In accordance with an embodiment, the method includes determining a second sequence of numbers of digits for encoding the respective integer coefficient values of the first sequence, the second sequence including, as first element, a first number of digits for encoding the first integer coefficient value of the first sequence, and as second and subsequent elements, constrained numbers of digits that are greater than or equal to respective minimum required numbers of digits for encoding the second and subsequent integer coefficient values of the first sequence. The constrained numbers of digits are such that any two successive elements of the second sequence do not differ from each other by more than a given threshold value. The method further includes encoding difference values between the successive elements of the second sequence; and encoding the integer coefficient values of the first sequence using the respective numbers of digits of the second sequence.

Various approaches for accelerating data access to a computer memory and predicate evaluation includes storing, in the computer memory, (i) base data as multiple base columns, (ii) multiple sketched columns each corresponding to a base column in the base data and having smaller code values compared thereto, and (iii) a compression map for mapping one or more base columns to the corresponding sketched column; applying the compression map to a query having a predicate; determining data on the sketched column that satisfies the predicate; and evaluating the predicate based at least in part on the determined data on the sketched column without accessing the base column in the base data.

A compressive sensing image sensor includes: a pixel array; and a readout circuit configured to receive pixel data on a shot image in an analogue form, and to process the pixel data, wherein the pixel array includes a plurality of blocks each having a plurality of pixels and arranged in an array form, wherein the circuit includes: a compressive sensing multiplexer to which a plurality of pixel data outputted from a corresponding block from among the plurality of blocks are inputted; an LFSR configured to arbitrarily select at least one pixel data from the plurality of pixel data inputted to the compressive sensing multiplexer; and a delta-sigma ADC configured to receive the at least one pixel data selected by the LFSR, to delta-sigma modulate the received at least one pixel data, and to generate compressive sensing data for restoring an image of the corresponding block from among the shot images.

Provided is an encoding/decoding technique according to which it is possible to perform encoding with a small average bit count, even for a series of integer values with a distribution that is significantly biased to a small value, including small values that are not zero values. The present invention includes an integer encoding unit that, for an input series of non-negative values x.sub.n, n∈{1, 2, . . . , N} (hereinafter referred to as “integer series”), obtains a one-bit code with a bit value of “x” as a code corresponding to L consecutive integer values 0 included in the integer series, L being an integer that is 2 or more, and obtains a K×x.sub.n-bit or a K×x.sub.n+1-bit code that includes at least one bit value “x” and at least one bit value “y” in the first bit to the K-th bit and in which the bit values of the K×(x.sub.n−1) bits from the end are “y”, as a code corresponding to a set composed of 0 to L−1 consecutive integer values 0 included in the integer series and one integer value x.sub.n other than 0.

The present invention provides a computer-implemented method, computer system and computer program product for data compression. According to the computer-implemented method, one or more data blocks on a data source to be replicated to a data target may be detected. Then, compression performance of a first compression dictionary may be evaluated. The first compression dictionary may be previously used to compress existing data on the data target. If the compression performance is lower than a preset performance threshold, a second compression dictionary may be generated based on the existing data on the data target. The data target may be updated based on the existing data and the one or more data blocks using the second compression dictionary.

Any one or both of an optical system with a structured lighting pattern and a structured detecting system having a plurality of regions with different optical characteristics are used. In addition, optical signals from an object to be observed through one or a small number of pixel detectors are detected while changing relative positions between the object to be observed and any one of the optical system and the detecting system, time series signal information of the optical signals are obtained, and an image associated with an object to be observed from the time series signal information is reconstructed.

A compression ratio (CR) adapter (CRA) for end-to-end data-driven compressive sensing (CS) reconstruction (EDCSR) frameworks is provided. EDCSR frameworks achieve state-of-the-art reconstruction performance in terms of reconstruction speed and accuracy for images and other signals. However, existing EDCSR frameworks cannot adapt to a variable CR. For applications that desire a variable CR, existing EDCSR frameworks must be trained from scratch at each CR, which is computationally costly and time-consuming. Embodiments described herein present a CRA framework that addresses the variable CR problem generally for existing and future EDCSR frameworks with no modification to given reconstruction models nor enormous additional rounds of training needed. The CRA exploits an initial reconstruction network to generate an initial estimate of reconstruction results based on a small portion of acquired image measurements. Subsequently, the CRA approximates full measurements for the main reconstruction network by complementing the sensed measurements with a re-sensed initial estimate.

A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values are included in the compressed attribute information file. An order for the points is determined based on a space filling curve, wherein an encoder and a decoder determine a same order for the points based on the space filling curve. Levels of detail are determined by sampling the ordered points according to different sampling parameters, and attribute values are predicted for the points in the levels of detail using the determined order. The encoder determines attribute correction values based on a comparison of the predicted values to an original value prior to compression. The decoder corrects the predicted attribute values based on received attribute correction values.