Читать книгу Graph Spectral Image Processing - Gene Cheung - Страница 37

If an image or subblock has several typical patterns, i.e. graphs, precomputing GFT bases for these graphs may be a reasonable choice to decrease the computational burden. This is because when we use them off the shelf, the computation cost reduces significantly as a result of decreased complexity, with a sacrifice of the storage cost for the GFT matrices and the cost of searching the optimal precomputed GFT from a given image. This precomputing strategy is popular in standard image processing. For example, in modern image/video coding standards, some precomputed transforms, such as DCT and discrete sine transform (DST) with various sizes, are utilized to represent image blocks as sparsely as possible.

Some precomputing methods have been proposed by Hu et al. (2015) and Zhang and Liang (2017), and they are mainly used for image compression. As expected, the GFT yields sparse transformed coefficients for piecewise smooth images/blocks. For those without such piecewise regions, conventional transforms like the DCT and DST are basically included as a set of precomputed bases.