Parametric motion modeling based on trilinear constraints for object-based video compression

We propose a new parametric motion model based on the so-called “trifocal tensor” representation, which captures rigid 3D motion of static scenes with a depth of field. The estimation of the trifocal tensor requires solution of a set of linear equations given at least seven point correspondences across three frames. The proposed parametric representation, called the trilinear model, is superior to other forms such as translational, affine, perspective, and bilinear models, because it can implicitly encode the depth of the scene and 3D motion of the scene/camera under perspective projection unlike others. A video object can thus be represented by its first VOP, a set of trifocal tensors and the corresponding prediction residues. Motion estimation and compensation based on the new parametric model are incorporated into the MPEG-4 Video Verification Model to compare its efficacy for object-based video compression with the state-of-the art motion compensation methods. Experimental results are provided to demonstrate the performance of the trilinear model for object-based video compression