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Recently, adaptive interpolation filter (AIF) for motion-compensated prediction (MCP) has received increasing attention. This letter studies the existing AIF techniques, and points out that making tradeoff between the two conflicting aspects: the accuracy of coefficients and the size of side information, is the major obstacle to improving the performance of the AIF techniques that code the filter coefficients individually. To overcome this obstacle, parametric interpolation filter (PIF) is proposed for MCP, which represents interpolation filters by a function determined by five parameters instead of by individual coefficients. The function is designed based on the fact that high frequency energies of HD video source are mainly distributed along the vertical and horizontal directions; the parameters are calculated to minimize the energy of prediction error. The experimental results show that PIF outperforms the existing AIF techniques and approaches the efficiency of the optimal filter.