Image interpolation with edge-preserving differential motion refinement

Motion estimation (ME) methods based on differential techniques provide useful information for video analysis, and moreover it is relatively easy to embed into them regularity constraints enforcing for example, contour preservation. On the other hand, these techniques are rarely employed for video compression since, though accurate, the dense motion vector field (MVF) they produce requires too much coding resource and computational effort. However, this kind of algorithm could be useful in the framework of distributed video coding (DVC), where the motion vector are computed at the decoder side, so that no bit-rate is needed to transmit them. Moreover usually the decoder has enough computational power to face with the increased complexity of differential ME. In this paper we introduce a new image interpolation algorithm to be used in the context of DVC. This algorithm combines a popular DVC technique with differential ME. We adapt a pel-recursive differential ME algorithm to the DVC context; moreover we insert a regularity constraint which allows more consistent MVFs. The experimental results are encouraging: the quality of interpolated images is improved of up to 1.1 dB w.r.t. to state-of-the-art techniques. These results prove to be consistent when we use different GOP sizes.