Zero padding is the transpose of truncation.

Surrounding a dataset by zeros (zero padding) is adjoint to throwing away the extended data (truncation). Let us see why this is so. Set a signal in a vector $\bold x$, and then make a longer vector $\bold y$by adding some zeros at the end of $\bold x$.This zero padding can be regarded as the matrix multiplication

$$\bold y\eq \left[ \begin{array} {c} \bold I \ \bold 0 \end{array} \right] \ \bold x$$ (3)

The matrix is simply an identity matrix $\bold I$above a zero matrix $\bold 0$.To find the transpose to zero padding, we now transpose the matrix and do another matrix multiply:

$$\tilde {\bold x} \eq \left[ \begin{array} {cc} \bold I & \bold 0 \end{array} \right] \ \bold y$$ (4)

So the transpose operation to zero padding data is simply truncating the data back to its original length.