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In this correspondence, we propose to add a lossless compression functionality into existing MPEGs by developing a new context tree to drive arithmetic coding for lossless video compression. In comparison with the existing work on context tree design, the proposed algorithm features in 1) prefix sequence matching to locate the statistics model at the internal node nearest to the stopping point, where successful match of context sequence is broken; 2) traversing the context tree along a fixed order of context structure with a maximum number of four motion compensated errors; and 3) context thresholding to quantize the higher end of error values into a single statistics cluster. As a result,the proposed algorithm is able to achieve competitive processing speed, low computational complexity and high compression performances, which bridges the gap between universal statistics modeling and practical compression techniques. Extensive experiments show that the proposed algorithm outperforms JPEG-LS by up to 24% and CALIC by up to 22%, yet the processing time ranges from less than 2 seconds per frame to 6 seconds per frame on a typical PC computing platform.