Optimal Schemes for Motion Estimation on Colour Image Sequences

Abstract

This paper describes a method for incorporating the chrominance information when estimating the motion in a colour image sequence. It is based on a maximum likelihood formulation of the motion estimation problem which assumes homogeneous additive Gaussian noise in each colour component, with known inter-field correlation statistics. The formulation is applied to the complex-wavelet-domain matching algorithm of Magarey and Kingsbury (see Proc. IEEE Int. Conf. on Image Processing, p.969-72, 1996). We also define a noise-decorrelating colour space transform which provides a simple implementation of the ML formulation in the wavelet domain. Results for noisy synthesised colour sequences with known motion and noise statistics demonstrate the superiority of the exact ML formulation over straightforward, unweighted three-component estimation, most noticeably in high noise conditions