The bunch sampling algorithm is based on the results of the structural identification of a generic MGRF model, i.e, the geometric structure and the placement rule derived from a training texture. The geometric structure is used as a mask for sampling texels from the training image, while the placement rule guides the placement of each texel into the synthetic texture. The bunch sampling is similar to the methods based on non-parametric sampling in also transferring and rearranging image signals sampled from the training image into the synthetic one.
For a regular texture, the locations of a texel in the training and the synthetic images are related in order to preserve the placement rule. That is, the two locations have a same relative shift with respect to the estimated placement gird tessellating both images. But for a stochastic texture without an explicit placement rule, each texel is copied to a random location in the synthetic image.
Such a retrieve-and-place procedure repeats until the entire image plane of a new texture is fully covered. Since each step is independent of any previous steps, the synthesis is non-causal.