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In the state-of-the-art video coding standard, H.264/AVC, the encoder is allowed to search for its prediction signals among a large number of reference pictures that have been decoded and stored in the decoder to enhance its coding efficiency. Therefore, the computation complexity of the motion estimation (ME) increases linearly with the number of reference picture. Many fast multiple reference frame ME algorithms have been proposed, whose performance, however, will be considerably degraded in the hardwired encoder design due to the macroblock (MB) pipelining architecture. Considering the limitations of the traditional four-stage MB pipelining architecture, two fast multiple reference frame ME algorithms are proposed here. First, on the basis of mathematical analysis, which reveals that the efficiency of multiple reference frames will be degraded by the relative motion between the camera and the objects, for the slow-moving MB, the authors adopt the multiple reference frames but reduce their search range. On the other hand, for the fast-moving MB, the first previous reference frame is used with the full search range during the ME processing. The mutually exclusive feature between the large search range and the multiple reference frames makes the computation saving performance of the proposed algorithm insensitive to the nature of video sequence. Second, following the Hadamard transform coefficient-based all_zeros block early detection algorithm, two early termination criteria are proposed. These methods ensure the pronounced computation saving efficiency when the encoded video has strong spatial homogeneity or temporal stationarity. Experimental results show that 72.7%-93.7% computation can be saved by the proposed fast algorithms with an average of 0.0899 dB coding quality degradation. Moreover, these fast algorithms can be combined with fast block matching algorithms to further improve their speedup performance.