Motion estimation is the process of determining motion vectors that describe the transformation from one 2D image to another; usually from adjacent frames in a video sequence. It is an ill-posed problem as the motion is in three dimensions but the images are a projection of the 3D scene onto a 2D plane. The motion vectors may relate to the whole image (global motion estimation) or specific parts, such as rectangular blocks, arbitrary shaped patches or even per pixel. The motion vectors may be represented by a translational model or many other models that can approximate the motion of a real video camera, such as rotation and translation in all three dimensions and zoom. More often than not, the term motion estimation and the term optical flow are used interchangeably. It is also related in concept to and stereo correspondence. In fact all of these terms refer to the process of finding corresponding points between two images or video frames. The points that correspond to each other in two views (images or frames) of a real scene or object are "usually" the same point in that scene or on that object. Before we do motion estimation, we must define our measurement of correspondence, i.e., the matching metric, which is a measurement of how similar two image points are. There is no right or wrong here; the choice of matching metric is usually related to what the final estimated motion is used for as well as the optimisation strategy in the estimation process. Each motion vector is used to represent a macroblock in a picture based on the position of this macroblock (or a similar one) in another picture, called the reference picture. The H.264/MPEG-4 AVC standard defines motion vector as: motion vector: a two-dimensional vector used for inter prediction that provides an offset from the coordinates in the decoded picture to the coordinates in a reference picture. The methods for finding motion vectors can be categorised into pixel based methods ("direct") and feature based methods ("indirect").