ADAPTIVE MULTI-MODALMOTION ESTIMATION FOR VIDEO COMPRESSION

An adaptive multi-modal motion estimation algorithm for video compression builds a luminance pyramid for each image of a moving image sequence. From the top level image of the luminance pyramid a global motion vector is determined between images at times t and t+n. The global motion vector is used as a pivot point and to define a search area. For each block of a current top level image a search for a match is carried out around the pivot point within the search area. The resulting block motion vectors serve as initial conditions for the next higher resolution level. A refinement process results in a displaced frame difference value (DFD) for each block as an error measure. If the error measure is small, the motion vector is chosen as the motion vector for the current block. If the error measure is large, then a search within the search area around a zero motion pivot point is conducted. The motion vector that results in the smallest error measure is chosen as the motion vector for the current block. The refinement and zero pivot searches are repeated for each level down to the full resolution base of the pyramid, resulting in the desired estimated motion vectors for the image.