Methods and devices for encoding a point cloud. A predictor-copy coding mode is described in which an encoder copies the predicted points for a sub-volume rather than encoding the occupancy data for the original points in the sub-volume. A predictor-copy coding mode flag is coded in the bitstream to signal to the decoder whether predictor-copy coding mode is active or inactive. The predictor-copy coding mode flag may only be coded for sub-volumes that are eligible to use the mode. Eligibility may be based on depth within the coding tree and/or size of the sub-volume. Predictor-copy coding mode has the result of early termination of a branch of the coding tree. Instead of decoding the occupancy for the remainder of the branch, a decoder copies the predicted points that are positioned within the sub-volume as the reconstructed points of the point cloud for that sub-volume.

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.

A device and computer-executable method is provided for adaptively determining a sampling scheme to be applied at a first sensor from among a plurality of sensors for sampling sensor data values corresponding to a signal. A sparsifying transform for a subsequent sampling time window of the first sensor is predicted, wherein the sparsifying transform is determined based on a predictive model of the sparsity of the signal. Moreover, a subsampling parameter for the subsequent sampling time window is determined. The subsampling parameter corresponds to a number of sensor data values to be acquired within the sampling time window. This subsampling parameter is determined based on the predicted sparsifying transform. Further determined is a compressive sampling scheme for the subsequent sampling time window of the first sensor. The compressive sampling scheme is determined based on the predicted sparsifying transform.

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.

A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. A processing/filtering element utilizes occupancy map information and/or auxiliary patch information to determine relationships between patches in image frames and adjusts encoding/decoding and/or filtering or pre/post-processing parameters based on the determined relationships.

An information handling system includes a processor configured to process a training data file to determine an optimal data compression algorithm. The processor may also perform a compression ratio analysis that includes compressing the training data file using data compression algorithms, calculating a compression ratio associated with each of the data compression algorithms, determining an optimal compression ratio from the compression ratio associated with the each data compression algorithm; and determining a desirable data compression algorithm associated with the training data file based on the optimal compression ratio. The processor may also perform a probability analysis that includes generating a symbol transition matrix based on the desirable data compression algorithm, extracting statistical feature data based on the symbol transition matrix, and generating probability matrices based on the statistical feature data to determine the optimal data compression algorithm for each segment of a working data file.

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.

A device and computer-executable method is provided for adaptively determining a sampling scheme to be applied at a first sensor from among a plurality of sensors for sampling sensor data values corresponding to a signal. A sparsifying transform for a subsequent sampling time window of the first sensor is predicted, wherein the sparsifying transform is determined based on a predictive model of the sparsity of the signal. Moreover, a subsampling parameter for the subsequent sampling time window is determined. The subsampling parameter corresponds to a number of sensor data values to be acquired within the sampling time window. This subsampling parameter is determined based on the predicted sparsifying transform. Further determined is a compressive sampling scheme for the subsequent sampling time window of the first sensor. The compressive sampling scheme is determined based on the predicted sparsifying transform.

Computer-implemented methods, systems, and devices to perform lossless compression of floating point format time-series data are disclosed. A first data value may be obtained in floating point format representative of an initial time-series parameter. For example, an output checkpoint of a computer simulation of a real-world event such as weather prediction or nuclear reaction simulation. A first predicted value may be determined representing the parameter at a first checkpoint time. A second data value may be obtained from the simulation. A prediction error may be calculated. Another predicted value may be generated for a next point in time and may be adjusted by the previously determined prediction error (e.g., to increase accuracy of the subsequent prediction). When a third data value is obtained, the adjusted prediction value may be used to generate a difference (e.g., XOR) for storing in a compressed data store to represent the third data value.

Methods of image compression are described. A stream of color image data is filtered with a prediction routine using a pixel neighborhood. The filtered stream of color image data is sorted with a block sorting routing. A version of the color image data is compressed based on the sorted and filtered stream of color image data.