Three-dimensional lateral prediction has two important
advantages over two-dimensional prediction.
The first
is that,
in a three-dimensional volume,
any output sample
is close to more samples in the input
than in the two-dimensional case.
Having more nearby samples allows better random noise attenuation.
Second, the assumption that events are linear
within a two-dimensional window or three-dimensional
subvolume can be relaxed.
As pointed out by
Chase 1992 and
Gulunay *et.al.* 1993,
events that are nonlinear in one direction but linear in another
are predicted exactly with a three-dimensional filter.

Prediction over a three-dimensional volume can be done with two passes of a two-dimensional process or with a true three-dimensional technique. Since two passes of either two-dimensional technique does not allow processors to take full advantage of the opportunities three-dimensional data presents, we have extended the two-dimensional techniques to true three-dimensional techniques using three-dimensional filters.

- The three-dimensional extension of t-x prediction
- The three-dimensional extension of f-x prediction
- Examples of three-dimensional lateral prediction

11/16/1997