We will show an example here of this simple prediction model and try to

argue why this more efficient to encode

the prediction error than the original signal.

So we're going to work with this image.

And we're going to use an one-dimensional prediction model.

So each line of this image is going to be processed separately.

So let's assume that here is how one of these lines in the image looks like.

Nothing particular about the values here.

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So, the prediction model is that the predicted

value at n equals the value of the signal at n minus 1.

So, if this, if we assume this is n, this

is n minus 1, therefore this is x n minus 1.

This is x of n.

According to this prediction model the predicted

value at n, is equal to x over minus 1.

And therefore the error that results is just x of n

minus the predicted value, so the value here.

So if we apply this model to this image, here's how the error looks like.

Since the original image is an eight bit image, here's 256 values.

The error has twice the dynamic range, therefore the

values range from 2, minus 255 to plus 255.

And for displaying purposes we have linearly mapped

this range to the 0 to 255 range.

Therefore the value here in the middle of 127 represents zero.

So as expected the, a lot of 127s, a lot of zeroes in the error image.

Because this model is quite accurate in the flat regions of the image.