Aspects of dynamic data compression selection are presented. In an example method, as uncompressed data chunks of a data stream are compressed, at least one performance factor affecting selection of one of multiple compression algorithms for the uncompressed data chunks of the data stream may be determined. Each of the multiple compression algorithms may facilitate a different expected compression ratio. One of the multiple compression algorithms may be selected separately for each uncompressed data chunk of the data stream based on the at least one performance factor. Each uncompressed data chunk may be compressed using the selected one of the multiple compression algorithms for the uncompressed data chunk.

Systems and methods for adaptive compression mode selection for memory buffers such as those used in or with a portable computing device (PCD) are presented. During operation of the PCD a first compression mode is selected for a buffer and the buffer is formatted to the first compression mode. Any access to the buffer by a component of the PCD, core of the PCD or software application running on the PCD is monitored. Based on the amount and/or type of access to the buffer, a second compression mode for the buffer is selected. The buffer is formatted to the second compression mode, providing a cost effective ability to adaptively format buffers based on the component(s), cores(s), and/or software application(s) accessing the buffers, and allowing for improving or optimizing bandwidth, memory footprint, resource conflict, power consumption, latency, and/or performance of component(s), core(s), or software application(s) accessing buffers as desired.

Apparatuses, methods, systems, and program products are disclosed for compression and distribution of meteorological data using machine learning. An apparatus includes a processor and a memory that stores code executable by the processor to receive a raw meteorological data set for a time frame, the raw meteorological data set comprising a plurality of dimensions. The code is executable by the processor to compress the raw meteorological data set using a machine learning encoding model to create an encoded meteorological data set that has a storage size that is smaller than a storage size of the raw meteorological data set, wherein the encoded meteorological data set can be decoded to create a decoded meteorological data set that is substantially similar to the raw meteorological data set. The code is executable by the processor to make the encoded meteorological data set accessible to one or more end users.

Block compression schemes used for image compression are susceptible to generating image blocks having redundant bit sets (i.e. a redundant bit combination), where one of the bit sets in the block is not meaningfully different from the other bit set in the block. As a result, one of the bit sets will be meaningless to a decompression scheme used to decompress the image and thus will not contribute to improving a quality of the decompressed image. The present disclosure provides a technique to exploit redundant bit combinations in a compressed representation of an image, including to exploit more than just the simple case of bit sets that are identical. Exploiting a redundant bit combination will allow an otherwise meaningless bit set to be used for some other discriminating purpose, which can allow for a higher image quality after decompression.