Global-motion estimation in image sequences of 3-D scenes for coding applications

A technique for global-motion estimation and compensation in image sequences of 3-D scenes is described in this paper. Each frame is segmented into regions whose motion can be described by a single set of parameters and a set of motion parameters is estimated for each segment. This is done using an iterative block-based image segmentation combined with the estimation of the parameters describing the global motion of each segment. The segmentation is done using a Gibbs-Markov model-based iterative technique for finding a local optimum solution to a maximum a posteriori probability (MAP) segmentation problem. The initial condition for this process is obtained by applying a Hough transform to the motion vectors of each block in the frame obtained by block matching. In each iteration, given a segmentation, the motion parameters are estimated using the least-squares (LS) technique. To obtain the final segmentation and the more appropriate higher-order motion model for each segment, a final stage of splitting/merging of segments is needed. This step is performed on the basis of maximum-likelihood decisions combined with the determination of the higher-order model parameters by LS. The incorporation of the proposed global-motion estimation technique in an image-sequence coder was found to bring about a substantial reduction in bit-rate without degrading the perceived quality or the PSNR.