TY - JOUR

T1 - MAP-Based motion refinement algorithm for block-based motion-compensated frame interpolation

AU - Choi, Dooseop

AU - Song, Wonseok

AU - Choi, Hyuk

AU - Kim, Taejeong

N1 - Publisher Copyright:
© 2015 IEEE.

PY - 2016/10

Y1 - 2016/10

N2 - A new motion vector field (MVF) refinement algorithm is proposed for block-based motion-compensated frame interpolation. Under the assumption that an observed MVF, such as the result of a block-based motion estimation (BME), is a degraded version of the true MVF, the true MVF is estimated from the observation through maximum a posteriori probability (MAP) estimation. To define the posterior probability of the true MVF, the degradation is modeled as a locally stationary additive Gaussian noise, so the variance of the noise represents the unreliability of the observed motion vector (MV). The noise variance is directly estimated from the observation vector and its select neighbors. The prior distribution of the true MVF is designed to rely on the distances between the MV and its neighbors and to properly smooth the false MVs in the observation. The MAP estimate of the true MVF is obtained via the iterative conditional mode method. The outcome is a set of iterative update equations, which produce the kth estimate of the true MV of a block by combining, according to the estimated noise variance, the observation and the neighboring (k-1)th estimates. Experimental results prove that the proposed algorithm achieves performances comparable with those of several existing MAP-based BME algorithms at a much lower computational complexity.

AB - A new motion vector field (MVF) refinement algorithm is proposed for block-based motion-compensated frame interpolation. Under the assumption that an observed MVF, such as the result of a block-based motion estimation (BME), is a degraded version of the true MVF, the true MVF is estimated from the observation through maximum a posteriori probability (MAP) estimation. To define the posterior probability of the true MVF, the degradation is modeled as a locally stationary additive Gaussian noise, so the variance of the noise represents the unreliability of the observed motion vector (MV). The noise variance is directly estimated from the observation vector and its select neighbors. The prior distribution of the true MVF is designed to rely on the distances between the MV and its neighbors and to properly smooth the false MVs in the observation. The MAP estimate of the true MVF is obtained via the iterative conditional mode method. The outcome is a set of iterative update equations, which produce the kth estimate of the true MV of a block by combining, according to the estimated noise variance, the observation and the neighboring (k-1)th estimates. Experimental results prove that the proposed algorithm achieves performances comparable with those of several existing MAP-based BME algorithms at a much lower computational complexity.

KW - Maximum a posteriori probability-Markov random field (MAP-MRF)

KW - Motion refinement (MR)

KW - Motioncompensated frame interpolation (MCFI)

UR - http://www.scopus.com/inward/record.url?scp=84991112047&partnerID=8YFLogxK

U2 - 10.1109/TCSVT.2015.2473275

DO - 10.1109/TCSVT.2015.2473275

M3 - Article

AN - SCOPUS:84991112047

SN - 1051-8215

VL - 26

SP - 1789

EP - 1804

JO - IEEE Transactions on Circuits and Systems for Video Technology

JF - IEEE Transactions on Circuits and Systems for Video Technology

IS - 10

M1 - 7225154

ER -