A hierarchical decimation lattice based on N-queen with an application for motion estimation

We present a novel technique, N-queen lattice, to spatially subsample a block of pixels. Although this lattice is pertinent to many applications, we present an application to speed up motion estimation with minimal loss of coding efficiency. The N-queen lattice is constructed to characterize spatial features in all directions. It can be hierarchically organized for motion estimation with variable nonsquare block size. Despite the randomized lattice structure, we demonstrate that it is possible to achieve compact data storage architecture for efficient memory access and simple hardware implementation. Our simulations show that the N-queen lattice is superior to several existing sampling techniques with improvement in speed by about N times and small loss in peak SNR.