A novel block matching algorithm for motion estimation

Conventional fast block-matching algorithms, such as TSS and DSWA/IS, are widely used for motion estimation in the low-bit-rate video coding. These algorithms are based on the assumption that when searching in the previous frame for the block that best matches a block in the current frame, the difference between them increases monotonically when a matching block moves away from the optimal solution. Unfortunately, this assumption of global monotonicity is often not valid, which can lead to a high possibility for the matching block to be trapped to local minima. On the other hand, monotonicity does exist in localized areas. In this paper, we proposed a new algorithm called Peano-Hilbert scanning search algorithm (PHSSA). With the Peano-Hilbert image representation, the assumption of global monotonicity is not necessary, while local monotonicity can be effectively explored with binary search. PHSSA selects multiple winners at each search stage, minimizing the possibility of the result being trapped to local minima. The algorithm allows selection of three parameters to meet different search accuracy and process speed: (1) the number of initial candidate intervals, (2) a threshold to remove the unpromising candidate intervals at each stage, and (3) a threshold to control when interval subdivision stops. With proper parameters, the multiple-candidate PHSSA converges to the optimal result faster and with better accuracy than the conventional block matching algorithms.