AI

A DYNAMIC MOTION VECTOR REFERENCING SCHEME FOR VIDEO CODING

Abstract

Video codec exploits temporal redundancy of video signal, in the form of motion compensated prediction, to achieve superior compression performance. The coding of motion vectors takes a large portion of the total rate cost. Prior research utilizes the spatial and temporal correlations of the motion field to improve the coding efficiency of the motion information. It typically constructs a candidate pool composed of a fixed number of reference motion vectors and allows the codec to select and reuse the one that best approximates the motion activity of the current block. This largely disconnects the entropy coding process from the true boundary conditions, since it is masked by the fix-length candidate list, and hence could potentially cause sub-optimal coding performance. An alternative motion vector referencing scheme is proposed in this work to fully accommodate the dynamic nature of the boundary conditions for compression efficiency. It adaptively extends or shortens the candidate list according to the actual number of available reference motion vectors. The associated probability model accounts for the likelihood that an individual motion vector candidate is used. A complementary motion vector candidate ranking system is also presented here. It is experimentally shown that the proposed scheme achieves considerable compression performance gains across all the test sets.