Formative motion estimation using affinity-cell neural network for application to MPEG-2

A neural network based motion-estimation technique is developed that is applicable to purely translational and as well as affine movements. It produces significantly lower motion compensated frame differences than the existing approaches, such as the logarithmic block matching and full search algorithms. This advantage, which is particularly dramatic when new and uncovered background is introduced into the image or new objects are formed, arises because a weighted combination of candidate macroblocks is used for reconstruction formulated in terms of a modified Hopfield neural network, the procedure consists of two stages: estimation of the neural network parameters, followed by estimation of affinities for the candidate macroblocks.