Abstract: A motion estimation algorithm was proposed using 2 bit-depth pixels. The reduction of pixel depth was first formalized by two successive steps, namely interval partitioning and interval mapping. The former is a many-to-one mapping which determines motion estimation performance, while the latter is a one-to-one mapping. A non-uniform quantization method was then presented to compute three initial thresholds of the interval partitioning. These initial thresholds were subsequently refined by using a membership function to solve the mismatch of pixel values near them caused by signal noise and so on. Afterwards, a matching criterion was discussed suitable for the motion estimation using 2 bit- depth pixels. A novel motion estimation algorithm was consequently addressed based on 2 bit-depth pixels and fuzzy quantization. To further predict the precision of the proposed algorithm, a bit resolution reduction error-motion vector precision model was built by exploiting the auto-correlation function. Extensive experimental results show that the proposed algorithm can always achieve high motion estimation precision for video sequences with various characteristics, especially for those with detailed scene and complex motion. Compared with traditional 2 bit motion estimation, the proposed algorithm gains 0.27 dB improvement in terms of average peak signal-to-noise ratio of motion compensation.