Configurable Complexity-Bounded Motion Estimation for Real-Time Video Encoding

Motion estimation (ME) is by far the main bottleneck in real-time video coding applications. In this paper, a configurable complexity-bounded motion estimation (CCBME) algorithm is presented. This algorithm is based on prediction-refinement techniques, which make use of spatial correlation to predict the search center and then use local refinement search to obtain the final motion field. During the search process, the ME complexity is ensured bounded through three configuration schemes: 1) configure the number of predictors; 2) configure the search range of local refinement; 3) configure the subset pattern of matching criterion computation. Different configuration leads to different distortion. Through joint optimization, we obtain a near-optimal complexity-distortion (C-D) curve. Based on the C-D curve, we preserve 6 effective configurable modes to realize the complexity scalability, which can achieve a good tradeoff between ME accuracy and complexity. Experimental results have shown that our proposed CCBME exhibits higher efficiency than some well-known ME algorithms when applied on a wide set of video sequences. At the same time, it possesses the configurable complexity-bounded feature, which can adapt to various devices with a wide range of computational capability for real-time video coding applications.