Abstract:A crude but commonly used technique for compressing ordered scientific data consists of simply retaining every sth datum (with a value of s = 10 generally the default) and discarding the remainder. Should the value of a discarded datum be required afterwards, an approximation is generated by linear interpolation of the two nearest retained values. Despite the widespread use of this and similar techniques, there is little by way of theoretical analysis of their expected performance. First, we quantify the accur… Show more
“…In practice, scientific data is always compressed with some loss because lossless techniques tend to have poor performance [1]. Unfortunately, any loss adds uncertainty to the data.…”
“…In practice, scientific data is always compressed with some loss because lossless techniques tend to have poor performance [1]. Unfortunately, any loss adds uncertainty to the data.…”
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