Highly-parallelized motion estimation for Scalable Video Coding
In this paper, we discuss the design of a highly-parallel motion estimator for real-time Scalable Video Coding (SVC). In an SVC, motion is commonly estimated bidirectionally and over various temporal distances. Current motion estimators are optimized for frame-by-frame estimation, and such estimators are designed without serious implementation constraints. To support efficient embedded applications, we propose a Highly Parallel Predictive Search (HPPS) motion estimator while preserving an accurate estimation performance. The motion estimation algorithm is optimized for processing on parallel cores and utilizes a novel recursive search strategy. This strategy is based on hierarchically increasing the temporal distance in the estimation algorithm while using the state of the previous hierarchical layer as an input. Due to the absence of local recursions in the algorithm, the proposed motion estimator has a constant computational load, regardless of video activity or temporal distance. We compared our proposed motion estimator to the well-known full search, ARPS3, 3DRS and EPZS motion estimators for the SVC case, and obtain a performance close to full search (0.2dB), while outperforming other algorithms in prediction.