1 research outputs found
An Adaptive Feature Based Low Power Motion Estimation Algorithm
Motion Estimation is one of the most power hungry
operations in video coding. While optimal search (eg. full search)methods give best quality, non optimal methods are often used in order to reduce cost and power. Various algorithms have been used in practice that trade off quality vs. complexity. Global elimination is an algorithm based on pixel averaging to reduce complexity of motion search while keeping performance close to that of full search. We propose an adaptive version of the global
elimination algorithm that extracts individual macro-block
features using Hadamard transform to optimize the search.
Performance achieved is close to the full search method and
global elimination. Operational complexity and hence power is reduced by 30% to 45% compared to global elimination method