<title>Fast block-matching algorithm using threshold-based half stop, cross search, and partial distortion elimination</title>

Abstract

New three-step search (NTSS) algorithm obtains good picture quality in predicted images with more reduced computation on the average. To reduce more the computation while keeping error performance compared with NTSS, this paper proposes a fast NTSS algorithm using unimodal error surface assumption correlation of causal adjacent matching errors, partial distortion elimination (PDE) algorithm and cross search algorithm. Proposing algorithm reduces less important checking points of the first step in the NTSS by using initial sum of absolute difference (SAD) and adaptive threshold of SAD. Instead of checking seventeen candidate points in the first step like the NTSS, our search algorithm starts with nine checking points according to the result of comparison between initial SAD and adaptive threshold of SAD. We get adaptive threshold of SAD according to the causal adjacent SADs. For more computational reduction without any degradation in prediction quality, we employ PDE and cross search algorithm. Therefore, we can apply this algorithm to variety of applications because the threshold is adaptive to the characteristics of each sequence. Experimentally, our algorithm shows good performance in terms of PSNR of predicted images and average-checking points for each block compared with the conventional NTSS and TSS algorithms.