Temporal Prediction of Motion Parameters with Interchangeable Motion Models

While the translational motion model remains predominant in motion compensation, better coding efficiency can be achieved by applying a higher order motion model in cases of non-translationally moving video content. But so far, temporal motion vector prediction has not been fully optimized in case of higher order motion compensation. In particular, if the motion model provided by the reference picture differs from the motion model of the current block, the temporal prediction is either not used at all or it is sub-optimal. Also, temporal predictors of a much smaller partition size might not provide the best suited motion parameter prediction to a larger current block. In order to overcome these issues, a new method of temporal motion prediction is introduced in this paper, allowing for flexible switching between motion models. The picture-wise dense translational motion vector field is calculated from both translational and higher order motion parameters with a configurable granularity of 4x4 pixel subpartitions down to pixelwise accuracy. Both a translational and a higher order motion parameter predictor are estimated from that vector field, thus giving the current block two alternatives to choose from. The proposed algorithm achieves rate reductions of about 2% on average compared to the previous higher order motion compensation system it is based on, now resulting in an average of around 20% efficiency gain for non-translational video content, compared to HEVC without such an option.