A statistical approach to motion vector field smoothing for block-based motion-compensated frame interpolation

This paper proposes a new approach to motion vector field smoothing for block-based motion-compensated frame interpolation (MCFI). Based on the assumption that an observed motion vector field, which is the result of a block-based motion estimation (BME), is a degraded version of the true motion vector field, we calculate the maximum a posteriori (MAP) estimate of the true motion vector field from the observed. The degradation and the true motion vector field are modeled as additive Gaussian noise and a Markov random field, respectively. Iterative conditional modes (ICM) method is used for calculating the MAP estimate. The experimental results show that the proposed algorithm not only smoothes MVFs but also preserves motion boundaries better than the existing methods.