Pel - Recursive Motion Estimation Algorithm

This paper presents a new pel recursive motion compensated prediction algorithm for video coding applications. The derivation of the algorithm is based on Recursive Least Squares (RLS) estimation that minimizes the mean square prediction error for each pel (picture element). A comparison with the modified Steepest-descent gradient estimation algorithm shows significant improvement in terms of mean-square prediction error performance. Introduction: Netravali and Robbins [1] developed a pel recursive spatio-temporal steepest-descent gradient technique in which the displacement of a pel was predicted from previously transmitted information. Since then various algorithms have been proposed to improve the performance of pel recursive motion estimation (PRME) techniques. The most important contribution was the modification of the steepest-descent algorithm which was introduced by Walker and Rao [2]. In this paper we present a simple but very efficient PRME algorithms that can significantly outperform the modified steepest-descent technique developed by Walker and Rao. Proposed Algorithm: For the sake of our analysis, we assume the translation movement of an object is in a plane parallel to the camera and illumination is uniform. We also assume the effect of uncovered background to be negligible. Under these assumptions, let S (x, y, t) denote the monochrome intensities at point x, y of a moving object in the image plane where its transnational