2:1 Candidate Position Subsampling Technique for Fast Optimal Motion Estimation

Abstract

The candidate position subsampling technique (CPST) basically chooses candidates in a search window at a sampling rate. The 2:1 CPST chooses half the candidates, and then selects one or more candidates that are considered as to be close to the optimal motion vector before conducting a fine search. The fine search is conducted by checking four neighbors of the chosen candidate(s) referred to as winner(s). The CPST can be combined with a fast optimal block-matching algorithm, such as the multilevel successive elimination algorithm (MSEA), in order to reduce the number of computations used in rejecting the nonbest candidate. We propose a new 2:1 CPST fitted to the MSEA. The proposed algorithm adopts a new condition for the winner which helps to find the best candidate efficiently. Moreover, a fast motion estimation step is used to reduce the number of computations of the MSEA, and the peak signal-to-noise ratio (PSNR) compensation step is adopted to guarantee that the PSNR performance of the proposed algorithm is very close to that of the full search. Experimental results show that the proposed algorithm reduces the computational loads of the MSEA by 47.26% on average with only -0.027 dB PSNR degradation in the worst case.