Analysis of fast block matching motion estimation algorithms for video super-resolution systems

In general, all the video super-resolution (SR) algorithms present the important drawback of a very high computational load, mainly due to the huge amount of operations executed by the motion estimation (ME) stage. Commonly, there is a trade-off between the accuracy of the estimated motion, given as a motion vector (MV), and the computational cost associated. In this sense, the ME algorithms that explore more exhaustively the search area among images use to deliver better MVs, at the cost of a higher computational load and resources use. Due to this reason, the proper choice of a ME algorithm is a key factor not only to reach real-time applications, but also to obtain high quality video sequences independently of their characteristics. Under the hardware point of view, the preferred ME algorithms are based on matching fixed-size blocks in different frames. In this paper, a comparison of nine of the most representative Fast Block Matching Algorithms (FBMAs) is made in order to select the one which presents the best tradeoff between video quality and computational cost, thus allowing reliable real-time hardware implementations of video super-resolution systems.