Abstract: The Motion Estimation(ME) in the video coding is the most complex and time-consuming one of all the processing stages. This paper extracts all the ME modules from multiple popular open source video codecs in order to evaluate and optimize their performance. In addition, a comprehensive input data set is constructed for these ME algorithms considering different video contents and resolutions. A quantitative analysis of runtime efficiency and microarchitecture characteristics are made for these algorithms by means of the profiling tool based on hardware performance counter, and the analysis exposes their performance difference on current mainstream processor architecture. The evaluation results show that for the input of complex and high-resolution video, the ME will consume the most time, while there are little difference between their low Instruction Level Parallelism(ILP). But the Last Level Cache(LLC) miss rate and branch misprediction rate of these algorithms are all rather low, which are respectively under 0.01% and 7%.