Regression-Based Motion Vector Field for Video Coding

This is the authors' abstract. We don't add key points for this paper.

In this paper, we study a method for compensating the non-translational motion behavior in video coding. The proposed method models the motion field of a prediction block based on the motion information of the neighboring blocks by using a linear regression approach. In order to provide a finer granularity of motion vectors the Regression-based Motion Vector Field (RMVF) method derives the motion field in 4×4 sub-block accuracy. Such approach generates a smooth and more realistic motion vector field inside the prediction block. The motion field generated with RMVF is then used as a new merge mode along with other merge modes in VTM-2.0 test model of the Versatile Video Coding (H.266/VVC) standard. The conducted experiments with JVET CTC sequences illustrate that the proposed RMVF method provides 0.77%, 0.19% and 0.41% bitrate reductions with random access (RA), low delay B (LDB) and low delay P (LDP) configurations, respectively. Furthermore, this method provides on average 0.65% bitrate saving for the 360° sequences in equirectangular projection format (ERP) with RA configuration.