A new scheme for object-oriented video compression and scene analysis based on motion tuned spatio-temporal wavelet family trajectory identification

Abstract

This work presents a new scheme for hybrid video compression. It is also aimed at showing the applicability of the scheme to scene analysis. The originality of this contribution is first to use spatio-temporal wavelet families tuned to motion. In this sense it differs from approaches based on motion unwarping then filtering with traditional wavelets. Here we process the warped signal and the motion parameters are acquired by wavelets tuned to specific parameters like rotation, velocity or acceleration, in addition to the traditional properties of scaling and translation of the wavelet transform. The other originality of this approach takes place in the fast construction of objects trajectories from the parameters acquired by the wavelet transform and from a model chosen for the trajectory estimation. We show in the final step how motion estimation can be deduced from the computed object trajectory.