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RICE is a mnemonic acronym for the four elements of a treatment regimen that was once recommended for soft tissue injuries: rest, ice, compression, and elevation. [1] It was considered a first-aid treatment rather than a cure and aimed to control inflammation . [ 2 ]
Golomb coding is a lossless data compression method using a family of data compression codes invented by Solomon W. Golomb in the 1960s. Alphabets following a geometric distribution will have a Golomb code as an optimal prefix code, [1] making Golomb coding highly suitable for situations in which the occurrence of small values in the input stream is significantly more likely than large values.
The RICE method is an effective procedure used in the initial treatment of a soft tissue injury. [6] Rest It is suggested that the patient take a break from the activity that caused the injury in order to give the injury time to heal. Ice The injury should be iced on and off in 20 minute intervals, avoiding direct contact of the ice with the skin.
In the {0.95, 0.05} example, a Golomb-Rice code with a four-bit remainder achieves a compression ratio of %, far closer to optimum than using three-bit blocks. Golomb-Rice codes only apply to Bernoulli inputs such as the one in this example, however, so it is not a substitute for blocking in all cases.
Cold compression is a combination of cryotherapy and static compression, commonly used for the treatment of pain and inflammation after acute injury or surgical procedures. [ 1 ] [ 2 ] Cryotherapy, the use of ice or cold in a therapeutic setting, has become one of the most common treatments in orthopedic medicine.
This optimization technique also is called PForDelta [1]. Although lossless compression methods like Rice, Golomb and PFOR are most often associated with signal processing codecs, the ability to optimize binary integers also adds relevance in reducing MEMS tradeoffs vs. operations.
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In information theory, an entropy coding (or entropy encoding) is any lossless data compression method that attempts to approach the lower bound declared by Shannon's source coding theorem, which states that any lossless data compression method must have an expected code length greater than or equal to the entropy of the source.