A hybrid model-based image coding system for very low bit-rate coding

This paper proposes a two-stage global motion estimation method and a hybrid coding algorithm for the model-based coding. In the first stage, global motion is estimated by the feature-based algorithm. The estimated result is further refined by the gradient method. The two-stage estimation algorithm is performed hierarchically to remove the influence of the local facial expression. In the estimation process, we also utilize the steerable pyramid to improve accuracy. The facial expression region is coded by the clip-and-paste method, and is predicted by a classified prototype coding technique. The prototype coding technique can greatly improve the coding efficiency for the facial expression. The areas, which are difficult to be described by generic models, are encoded by the proposed hierarchical motion segmentation algorithm. We segment the image to different moving areas which can be modeled by affine models in a split-and-merge manner. The segmented results with the estimated affine models can give good prediction for the unmodeled regions. Computer simulation results show that the proposed complete model-based coding scheme gives very good performance in terms of peak signal-to-noise ratio and compression ratio