The computational complexity of motion estimation between video frames for video coding remains a significant challenge even with current computing power. An important recent advance in the development of efficient motion estimation algorithms is the use of image registration in the estimation of global motion parameters for object-based video coding. However, the main disadvantage of this approach is the increased computational complexity required to estimate the parameters which define the more complex motion models. In this paper, we propose a multi-patch based low complexity global motion estimation (GME) algorithm which uses the relatively new Image Interpolation Algorithm (I2A). Experimental results show that our proposed algorithm achieves the same registration accuracy as the standard GME approach but with significantly less iterations required.

An important recent application of image registration is the estimation of global motion parameters for object-based video coding. However, the main disadvantage of standard approaches to global motion estimation (GME) is the increased computational complexity with the higher degree of motion models when compared to block-based local motion estimation approaches. In this paper, we propose a new low complexity GME algorithm. In our proposed algorithm, full-precision images are replaced with 1 bit-per-pixel images which allows many of the arithmetic operations in the standard GME approach to be replaced with logic operations. Experimental results show that our proposed algorithm achieves the same registration accuracy as the standard GME approach but with significantly reduced computational complexity. Our results also demonstrate the superior performance of the proposed algorithm when compared with previously proposed low-complexity GME approaches.

One important recent application of image registration has been in the estimation of global motion parameters for object-based video coding. A limitation of current global motion estimation approaches is the additional complexity of the gradient-descent optimization that is typically required to calculate the optimal set of global motion parameters. In this paper we propose a new low-complexity algorithm for global motion estimation. The complexity of the proposed algorithm is reduced by performing the majority of the operations in the gradient-descent optimization using logic operations rather than full-precision arithmetic operations. This use of logic operations means that the algorithm can be implemented much more easily in hardware platforms such as field programmable gate arrays (FPGAs). Experimental results show that the execution time for software implementations of the new algorithm is reduced by a factor of almost four when compared to existing fast implementations without any significant loss in registration accuracy.