Wavelet Pyramid Based Multi-Resolution Bilateral Motion Estimation for Frame Rate Up-Conversion

Ran LI  Hongbing LIU  Jie CHEN  Zongliang GAN  

IEICE TRANSACTIONS on Information and Systems   Vol.E99-D    No.1    pp.208-218
Publication Date: 2016/01/01
Publicized: 2015/06/03
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7027
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
motion-compensated frame rate up-conversion,  bilateral motion estimation,  multi-resolution search,  wavelet pyramid,  

Full Text: PDF>>
Buy this Article

The conventional bilateral motion estimation (BME) for motion-compensated frame rate up-conversion (MC-FRUC) can avoid the problem of overlapped areas and holes but usually results in lots of inaccurate motion vectors (MVs) since 1) the MV of an object between the previous and following frames is more likely to have no temporal symmetry with respect to the target block of the interpolated frame and 2) the repetitive patterns existing in video frame lead to the problem of mismatch due to the lack of the interpolated block. In this paper, a new BME algorithm with a low computational complexity is proposed to resolve the above problems. The proposed algorithm incorporates multi-resolution search into BME, since it can easily utilize the MV consistency between two adjacent pyramid levels and spatial neighboring MVs to correct the inaccurate MVs resulting from no temporal symmetry while guaranteeing low computational cost. Besides, the multi-resolution search uses the fast wavelet transform to construct the wavelet pyramid, which not only can guarantee low computational complexity but also can reserve the high-frequency components of image at each level while sub-sampling. The high-frequency components are used to regularize the traditional block matching criterion for reducing the probability of mismatch in BME. Experiments show that the proposed algorithm can significantly improve both the objective and subjective quality of the interpolated frame with low computational complexity, and provide the better performance than the existing BME algorithms.