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Next, the X and Y data is passed through a predictor to attempt to remove any redundancy. Basically, the goal of this stage is to make the X and Y arrays contain the smallest possible values while still remaining decompressible.

This stage is what separates one compression scheme from another. There are virtually countless ways to do this. Here is a sample using simple linear algebra:. Then, these predicted values are compared with the actual value and the difference error is what gets sent to the next stage for encoding. Most good predictors are adaptive, so that they adjust to how "predictable" the data currently is.

For example, let's use a factor 'm' that ranges from 0 to 0 is no prediction and is full prediction. After each prediction, m is adjusted up or down depending on whether the prediction was helpful or not. So in the previous example, what leaves the predictor is this:. Using different prediction equations and using multiple passes through the predictor can make a fairly substantial difference in compression level.

Here is a quick list of some prediction equations as shown in the Shorten technical documentation for different orders :. Stereo record of neon tube turned on. Zazvurek June 9th, downloads 32 comments. Window Opening Squeaks Slide. Window Opening Squeaks S RutgerMuller March 30th, downloads 5 comments. Old typewriter. Horn November 18th, downloads 23 comments.

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