one with minimum cost function Set the new search origin to the above picked location Set the new step size as S = S/2 Repeat the search procedure until S = 1 Thus this algorithm checks 17 points for each macro-block and the worst-case scenario involves checking 33 locations, which is still much faster than TSS Simple and Efficient Search The idea behind TSS is that the error surface due to motion in every macro block is unimodal. A unimodal surface is a bowl shaped surface such that the weights generated by the cost function increase monotonically from the global minimum. However a unimodal surface cannot have two minimums in opposite directions and hence the 8 point fixed pattern search of TSS can be further modified to incorporate this and save computations. SES is the extension of TSS that incorporates this assumption. SES algorithm improves upon TSS algorithm as each search step in SES is divided into two phases: • First Phase : • Divide the area of search in four quadrants • Start search with three locations, one at center (A) and others (B and C), S=4 locations away from A in orthogonal directions • Find points in search quadrant for second phase using the weight distribution for A, B, C: • If (MAD(A)>=MAD(B) and MAD(A)>=MAD(C)), select points in second phase quadrant IV • If (MAD(A)>=MAD(B) and MAD(A)<=MAD(C)), select points in second phase quadrant I • If (MAD(A)<MAD(B) and MAD(A)<MAD(C)), select points in second phase quadrant II • If (MAD(A)<MAD(B) and MAD(A)>=MAD(C)), select points in second phase quadrant III • Second Phase: • Find the location with lowest weight • Set the new search origin as the point found above • Set the new step size as S = S/2 • Repeat the SES search procedure until S=1 • Select the location with lowest weight as motion vector SES is computationally very efficient as compared to TSS. However the peak signal-to-noise ratio achieved is poor as compared to TSS as the error surfaces are not strictly unimodal in reality. Four Step Search Four Step Search is an improvement over TSS in terms of lower computational cost and better peak signal-to-noise ratio. Similar to NTSS, FSS also employs center biased searching and has a halfway stop provision. The algorithm runs as follows: Start with search location at center Set step size S = 2, (irrespective of search parameter p) Search