Formative motion estimation for translational, shear and zoom sequences

Digital television seeks to reduce the bit rate through the elimination of interframe redundancy. The more accurate the motion estimation, the lower the encoded bit rate with the same picture quality, resulting in more channels the provider can deliver to the consumer at a reduced price. We develop a neural network motion estimation technique which produces 4 to 11 dB improvement of peak signal to noise ratio (PSNR) over the other methods, thus creating the potential for improved compression. In contradistinction to the other methods, there is no need to carry out fractional-pixel interpolation. The technique is applicable to purely translational, shear, and zoom sequences. Further, it is formative in that it can form objects that are formed in the present frame and are non-existent in the previous frame