Current sub-pixel motion estimation algorithm is time and memory-consuming when performing image compression and communication. So we propose a selective interpolation method for sub-pixel motion estimation. We applied selective interpolations after estimating a candidate for sub-pixel accuracy motion vector from the simplest mathematical model. According to simulation results, the proposed method attains nearly the same performance as the full-search for half-pixel motion estimation with much lower computational complexity.

In this letter, we propose an error surface modeling-based segmentalized motion estimation for video coding. We proposed two algorithms previously, one was MBQME [1] and the other is HMBQME [2]. However, these algorithms are not based on locally quadratic MC prediction errors around an integer-pixel motion vector and the hypothesis that the local error plane is a convex function. Therefore, we propose an error surface considered segmentalized modeling algorithm. In this scheme, the tendency of the error surface is first assessed. Using the Sobel operation at the error surface, we classify the error surface region as plain or textured. For plain regions, conventional MBQME is appropriate as the quarter-pixel motion estimation method. For textured regions, we search the additional interpolation points for more accurate modeling. After the interpolation, we perform double precision mathematical modeling so as to find the best motion vector (MV). Experiments show that the proposed scheme has better PSNR performance than conventional modeling algorithms with minimum operation time.