A hierarchical N-queen decimation lattice and hardware architecture for motion estimation

Abstract

A subsampling structure, an N-Queen lattice, for spatially decimating a block of pixels is presented. Despite its use for many applications, we demonstrate that the N-Queen lattice can be used to speed up motion estimation with nominal loss of coding efficiency. With a simple construction, the N-Queen lattice characterizes the spatial features in the vertical, horizontal, and diagonal directions for both texture and edge areas. Especially in the 4-Queen case, every skipped pixel has the minimal and equal distance of unity to the selected pixel. It can be hierarchically organized for variable nonsquare block-size motion estimation. Despite the randomized lattice, we design 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 (PSNR). The loss in PSNR is negligible for slow-motion video sequences and is less than 0.45 M at worst for high-motion estimation sequences.