An efficient dynamic multiple-candidate motion vector approach for GPU-based hierarchical motion estimation

Hierarchical or pyramid search is a widely used approach in motion estimation, a most expensive function in video encoding, for its low computational complexity and high efficiency. In this approach, multiple down-sampled resolutions from video frames are created. An initial motion estimation is quickly made at a lowest resolution. The final motion estimation result is achieved by propagating the initial estimation towards the original resolution. GPU or General purpose GPU embedded hundreds of number of SIMD-based cores is best suitable for motion estimation, especially with full-search-based approaches as the process can be efficiently parallelized. However, a common fundamental drawback of the hierarchical search is the erroneous estimation from the reduced resolutions may cause the final motion estimation inaccurate. Multiple-candidate motion vector approaches are proposed, however, they lack a mechanism to select the best multiple-candidate schemes considering diverse video encoding characteristics. In this paper we analyse and verify the computational complexity of the hierarchical search using NVIDIA´s GPU with realistic workloads. Based on this analysis, we propose an efficient dynamic multiple-candidate motion vector approach to dynamically select best multiple-candidate motion vector schemes at runtime. This approach can achieve highest possible speedups and satisfy a desire motion estimation efficiency. Experiments on realistic workloads show the dynamic scheme selection outperforms the fixed scheme selection based on profiling.