Comparative analysis of motion estimation algorithms on slow, medium and fast video sequences

Motion estimation is the key component in the entire process of video compression. The growing need of compression has led to the development of various fast block based motion estimation algorithms. Based on the motion activity in the consecutive frames video sequences could be broadly divided in to three categories- slow, medium and fast video sequences. The current work gives a comparative analysis of various motion estimation algorithms on all the three categories. The motion estimation algorithms which are analyzed are Full Search (FS), Three Step Search (TSS), New Three Step Search (NTSS), Diamond Search (DS) and Adaptive Rood Pattern Search (ARPS). The performance of these algorithms is considered in terms of Peak to Signal Ratio (PSNR), number of computations and the time required to find motion vectors per frame. All the algorithms are compared and also the suitability of various algorithms is established for each of the categories of slow, medium and fast video sequences. The conclusions are drawn for the overall optimization of performance based on the mentioned factors.